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BOARD STANDING COMMITTEES - 09122024 - Head Start Cte Agenda Pkt
CONTRA COSTA COUNTY AGENDA Head Start Committee Supervisor John Gioia, Chair Supervisor Ken Carlson, Vice Chair 11780 San Pablo Ave., Suite D, El Cerrito | 2255 Contra Costa Blvd., Suite 202, Pleasant Hill | Zoom: https://us06web.zoom.us/j/89752281411? pwd=oO7YjWVnMFh5bLpT9wErJInL O5zNcw.L-kq6aPviALVQ-zi | Call in: +1 669 444 9171 Meeting ID: 897 5228 1411 9:00 AMMonday, September 16, 2024 1.The public may attend this meeting in person at either above location . The public may also attend this meeting remotely via Zoom or call-in. 2.Agenda Items: Items may be taken out of order based on the business of the day and preference of the Committee. 3.Call to Order 4.Public comment on any item under the jurisdiction of the Committee and not on this agenda (speakers may be limited to two (2) minutes). 5.APPROVE the Board of Supervisors Head Start Committee Minutes of July 22, 2024 24-2930 Head Start Committee Meeting Minutes of July 22 2024Attachments: 6.DISCUSS and Accept the Head Start Program Update.24-2910 Head Start Update_BOS Sept 2024 FINAL Mental Health Report on ACF Information Memo 24-01 Information Memorandum: acf-ohs-im-24-01 Absenteeism in Head Start and Children's Academic Learning ECRQ Absenteeism Article Why are children absent from preschool Period 2_ 2023-2024 Semi-Annual Monitoring Report Summary_ Attachments: The next meeting is currently scheduled for November 18, 2024 at 9am. Adjourn Page 1 of 2 1 Head Start Committee AGENDA September 16, 2024 General Information This meeting provides reasonable accommodations for persons with disabilities planning to attend a the meetings. Contact the staff person listed below at least 72 hours before the meeting. Any disclosable public records related to an open session item on a regular meeting agenda and distributed by the County to a majority of members of the Committee less than 96 hours prior to that meeting are available for public inspection at 1025 Escobar St., 4th Floor, Martinez, during normal business hours. Staff reports related to items on the agenda are also accessible on line at www.co.contra-costa.ca.us. HOW TO PROVIDE PUBLIC COMMENT: Persons who wish to address the Committee during public comment on matters within the jurisdiction of the Committee that are not on the agenda, or who wish to comment with respect to an item on the agenda, may comment in person, via Zoom, or via call-in. Those participating in person should offer comments when invited by the Committee Chair. Those participating via Zoom should indicate they wish to speak by using the “raise your hand” feature in the Zoom app. Those calling in should indicate they wish to speak by pushing *9 on their phones. Public comments generally will be limited to two (2) minutes per speaker. In the interest of facilitating the business of the Board Committee, the total amount of time that a member of the public may use in addressing the Board Committee on all agenda items is 10 minutes. Your patience is appreciated. Public comments may also be submitted to Committee staff before the meeting by email or by voicemail. Comments submitted by email or voicemail will be included in the record of the meeting but will not be read or played aloud during the meeting. For Additional Information Contact: Christina Castle-Barber, 925-608-8819 Page 2 of 2 2 CONTRA COSTA COUNTY Staff Report 1025 ESCOBAR STREET MARTINEZ, CA 94553 File #:24-2930 Agenda Date:9/16/2024 Agenda #:5. HEAD START COMMITTEE Meeting Date: September 16, 2024 Subject: APPROVAL of Minutes of July 22, 2024 Submitted For: Marla Stuart Department: Employment and Human Services Presenter: Scott Thompson, CSB Director Contact: Christina Castle-Barber, 925-608-8819 Recommendation(s)/Next Step(s): APPROVE the Board of Supervisors Head Start Committee Meeting Minutes of July 22, 2024. CONTRA COSTA COUNTY Printed on 9/13/2024Page 1 of 1 powered by Legistar™3 CONTRA COSTA COUNTY Committee Meeting Minutes Head Start Committee Supervisor John Gioia, Chair Supervisor Ken Carlson, Vice Chair 9:00 AM11780 San Pablo Ave., Suite D, El Cerrito | 2255 Contra Costa Blvd., Suite 202, Pleasant Hill | Zoom: https://us06web.zoom.us/j/89752281411? pwd=dKWn0JnQk9BHjwOp3u9aEb2wAcew2r .1 | Call in: 1-669-444-9171 passcode: 386964 Monday, July 22, 2024 1.The public may attend this meeting in person at either above location . The public may also attend this meeting remotely via Zoom or call-in. 2.Agenda Items: Items may be taken out of order based on the business of the day and preference of the Committee. 3.Call to Order The Chair called the meeting to order at 9 AM. 4.Public comment on any item under the jurisdiction of the Committee and not on this agenda (speakers may be limited to two (2) minutes). 5.DISCUSS and ACCEPT the monthly update on the activities and oversight of the County's Head Start Program. Supervisor Gioia requested more information on Mental Health Services to be included in next presentation in August . A motion was made that this item be ACCEPTED . The motion carried by a unanimous vote. Motion:Carlson GioiaSecond: 6.APPROVE the Minutes of May 20, 2024. A motion was made by Carlson, seconded by Gioia, that this item be APPROVED . The motion carried by a unanimous vote . Motion:Carlson GioiaSecond: 7.Adjourn Next meeting will be held on September 16, 2024 at 9 am. Page 1 of 2 4 Head Start Committee Committee Meeting Minutes July 22, 2024 General Information: This meeting provides reasonable accommodations for persons with disabilities planning to attend a the meetings. Contact the staff person listed below at least 72 hours before the meeting. Any disclosable public records related to an open session item on a regular meeting agenda and distributed by the County to a majority of members of the Committee less than 96 hours prior to that meeting are available for public inspection at 1025 Escobar St., 4th Floor, Martinez, during normal business hours. Staff reports related to items on the agenda are also accessible on line at www.co.contra-costa.ca.us. HOW TO PROVIDE PUBLIC COMMENT: Persons who wish to address the Committee during public comment on matters within the jurisdiction of the Committee that are not on the agenda, or who wish to comment with respect to an item on the agenda, may comment in person, via Zoom, or via call-in. Those participating in person should offer comments when invited by the Committee Chair. Those participating via Zoom should indicate they wish to speak by using the “raise your hand” feature in the Zoom app. Those calling in should indicate they wish to speak by pushing *9 on their phones. Public comments generally will be limited to two (2) minutes per speaker. In the interest of facilitating the business of the Board Committee, the total amount of time that a member of the public may use in addressing the Board Committee on all agenda items is 10 minutes. Your patience is appreciated. Public comments may also be submitted to Committee staff before the meeting by email or by voicemail. Comments submitted by email or voicemail will be included in the record of the meeting but will not be read or played aloud during the meeting. For Additional Information Contact: Christina Reich Page 2 of 2 5 CONTRA COSTA COUNTY Staff Report 1025 ESCOBAR STREET MARTINEZ, CA 94553 File #:24-2910 Agenda Date:9/16/2024 Agenda #:6. HEAD START COMMITTEE Meeting Date: September 16, 2024 Subject: Head Start Program Update Presentation Submitted For: Marla Stuart Department: Employment and Human Services Referral No: Referral Name: Presenter: Scott Thompson Contact: Christina Castle-Barber, 925-608-8819 Recommendation(s)/Next Step(s): DISCUSS and ACCEPT the Head Start Program Update Presentation. CONTRA COSTA COUNTY Printed on 9/13/2024Page 1 of 1 powered by Legistar™6 1 September 16 and 24, 2024 Marla Stuart, MSW, PhD, EHSD Director and Head Start Executive Director Scott Thompson, Head Start Director, Community Services Bureau info@ehsd.cccounty.us | (925) 608-4800 Early Childhood Education Program Update 7 2 Outline •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation 8 33 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation 9 4 Budget Approved by Category 10 5 Slots per District A B C D E F G #0-5 <130% FPL %0-5 <130% FPL CCC Total Slots Directly Operated Partners % of Total CCC 1 Gioia 4,737 27%421 341 80 35% 2 Andersen 891 5%0 0 0 0% 3 Burgis 3,956 22%240 68 172 20% 4 Carlson 2,480 14%20 0 20 2% 5 Glover 5,758 32%371 182 189 31% County-wide 149 149 12% Totals 17,822 100%1,201 591 610 100% 11 6 Slots per Provider Recipient HS Preschool Early Head Start Total Contra Costa County 764 437 1,201 Mexican American Opportunity Foundation 97 116 213 The Unity Council 88 132 220 Total 949 685 1,634 12 7 Service Map 13 8 Slots per Partner Partner Agency Total Slots Filled Slots Vacant Slots Aspiranet 149 147 2 Crossroads 20 11 9 KinderCare 152 65 87 Tiny Toes 32 20 12 YMCA 257 257 0 14 9 Enrollment per District CCC Total Slots Filled Slots Vacant Slots Enrollment % 1 Gioia 421 334 87 79% 2 Andersen 0 0 0 0% 3 Burgis 240 136 104 57% 4 Carlson 20 11 9 55% 5 Glover 371 274 97 74% County-wide 149 147 2 99% Totals 1,201 902 299 75% 15 10 Strategies for Reaching Enrollment Target •Contracting support for staff recruitment •Creation of new administrative positions to carry out staff recruitment •Negotiating teacher salary increase •New contract with YMCA •New partnership with KinderCare Location in Pittsburg for 40 slots •Ramp up child recruitment – All hands on deck approach 16 11 Transition Activities •Region IX Transition Meeting: August 26, 2024 •Transition Activities: •Children transitioning to new HS recipient: 163 •Assessing inventory transfer (federal supplies and equipment) •Contra Costa County Collaboration Meeting: September 30, 2024 17 12 New FTE +8 Comprehensive Services to reduce caseloads +8 Teachers to expand slots +4 Clerical support (enrollment, comprehensive services) +1 Fiscal support +12 Institutional services workers +2 Teacher recruitment +35 TOTAL 18 1313 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 19 14 Report on Supporting Mental Health ACF STRATEGY CCC CURRENT CCC ADDITIONS 1. Focus on Social Determinants of Well-Being Family Partnership Agreements, Parent Resiliency Training, Wellness Activities & Events Implement Screener at Enrollment; Create Parent Cafes 2. Collaborate with Parents to Promote Well-Being Parenting classes, Family Support Groups, Resources & Referrals Create Peer Support Groups Facilitated by a Clinician 3. Promote Staff Well-Being with Learning Opportunities Reflective Supervision, Employee Assistance Program, Wellness Team & Wellness activities Create Spaces at each Center for Staff Check-Ins and Wellness Activities 4. Implement Positive Strategies to Address Behavior Teaching Pyramid approach, supportive learning environments, multi-tiered approach to managing behavior. Provide all classrooms/parents with Teaching Pyramid Kits 5. Provide Supports for Effective Classroom Management Collaboration with Early Childhood Mental Health Program (ECMHP) & Education Team, Ages & Stages Assessment, Second Step Curriculum Hire a Behavioral Health Therapist to support children. 6. Employ Infant and Early Childhood MH Consultation Contracted services with ECMHP, with Medi-Cal reimbursement, consultation by The California Infant and Early Childhood Mental Health Consultation Network Implement telehealth consultation for MH Services 7. Ensure Consultants Comply with Performance Standards Contract with ECMHP is tied to Head Start Performance Standards; ongoing training via community partnerships. Send MH Consultants to State and National Conferences 8. Build Community Partnerships to Access MH Services Participation in multiple partnership efforts with First 5, CoCoKids, C.O.P.E., ECMHP, Vistability, and Contra Costa Behavioral Health. Hire Trauma Response Expert to enhance direct support. Report: Supporting Mental Health and ACF-OHS-IM-24-01 20 15 Research on Attendance RESEARCH FINDINGS RECOMMENDATIONS More missed more days of school led to fewer gains in math and literacy during the preschool year, especially for chronically absent. Excessive absenteeism detracts from the potential benefits of quality preschool education, especially for children entering Head Start with a less developed skill set. Children who attended center-based care in prekindergarten had lower odds of being chronically absent in kindergarten. Children with a close relationship with their teacher and positive experiences in the classroom were more likely to want to attend. Parents with perception that the classroom is a positive experience for their child were more likely to ensure that their child was present as much as possible. Studies Ansari & Purtell (2019), Gottfried (2015), and Ansari & Purtell (2022) Ensure that families understand the importance and impact of regular attendance: •Stress that our program is an education program. •Share information on the negative effects of missed days with families. •Celebrate and encourage regular attendance by acknowledging children with 100% attendance and the classrooms with high attendance percentages. Help the teachers and site staff build positive relationships with the children and the families: •Nurture the teacher – child relationship. •Encourage Staff to make connections with the families and build positive feelings about the site and classroom. •Discuss barriers to attendance with families and help find solutions. 21 1616 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 22 17 Infants-School Readiness Data 23 18 Toddlers-School Readiness Data 24 19 Preschool-School Readiness Data 25 20 Pre-Kindergarten-School Readiness Data 26 21 October is Head Start Awareness Month 27 2222 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 28 23 Enrollment 29 24 Attendance 30 25 Meals Line = Average Meals per Day of Operation Circles = Days of Operation 31 26 Teacher and Site Supervisor Vacancies Lines = Vacancy Rate 32 2727 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 33 28 All Childcare Budget Overview $5.2 million 3,693 children 34 29 Early Head Start / Head Start * Note:Budget revised to show PY 2022 Carryover of $3,599,831 35 30 Head Start Credit Card Expenditures 36 3131 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 37 32 Daily Playground Safety Checklist 32 * * Note:Revised monitoring tools were implemented effective Nov 7, 2023 Example from August Presentation 38 33 Health and Safety Compliance * Indicators with non-compliances over 10% in July 2024: None Period 2 Semi-Annual Monitoring Report; * Note:Revised monitoring tools were implemented effective Nov 7, 2023 39 34 Unusual Incidents 40 3535 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 41 36 Office of Head Start Communications No Communications in July 2024 42 3737 •New Grant •Follow-up to BOS Questions •Annual Information & Updates •Childcare Center Services •Budget •Monitoring •Region IX Communication •Recommendation Placeholder – Insert CSB-specific photo here 43 38 Recommendation CONSIDER accepting monthly update on the activities and oversight of the County's Head Start Program and APPROVE and AUTHORIZE the Employment and Human Services Department to accept grant funding from U.S. Health and Human Services Administration for Children and Families in an amount not to exceed $20,356,394 for the period of September 1, 2024, through June 30, 2025, as recommended by the Employment and Human Services Director, and provide guidance. 44 1 | Page Contra Costa County Employment and Human Services Department ACF-OHS-IM-24-01 Report, CSB Mental Health Unit 9-12-2024 Michelle Mankewich and Jacquline Lopez Padilla REPORT BY EMPLOYMENT & HUMAN SERVICE DEPARTMENT COMMUNITY SERVICES BUREAU ON ACF-OHS-IM-24-01: STRATEGIES AND RECOMMENDATIONS FOR SUPPORTING MENTAL HEALTH On May 9, 2024, the Administration for Children and Families issued an information memorandum entitled Strategies and Recommendations for Supporting Mental Health. This memorandum was issued in response to a rise in developmental and behavioral concerns, higher rates of staff turnover, and limited availability of specialized mental health services. It includes eight strategies that are arranged according to three categories: Mental Health Promotion, Prevention Services and Supports, and Access to Mental Health Treatment. After reviewing what our current program is currently doing, we determined that we are in full compliance with Mental Health regulations and this IM has prompted us to think of other activities we could be doing. Below each strategy, we have listed what the Community Services Bureau (CSB) is currently doing and what we will be adding to our menu of services. INCREASE MENTAL HEALTH PROMOTION STRATEGIES TO INCREASE MENTAL HEALTH PROMOTION WHAT CSB IS CURRENTLY DOING WHAT CSB IS ADDING STRATEGY 1. A focus on social determinants of health, or the conditions in which individuals are born, grow, live, work and age, can lead to better mental health outcomes and prevent future mental illness. To promote social conditions that support family well-being, such as family safety, health, and economic stability, programs are encouraged to develop innovative two- generation approaches that leverage community partnerships and address prevalent needs of children and families (45 CFR §1302.50(a–b)). a) 995 families completed a Strengths Based Family Partnership agreement, which includes all social determinants of health in the 2023-2024 program year. Resources and referrals are provided for all areas to improve quality of life. b) Mental Health Consultants introduce themselves during program events such as Parent Resiliency training and Policy Council meetings. (See Attachment 1) c) Time is devoted for wellness activities at site-based parent meetings and Head Start Policy Council Meetings. (See Attachment 2) d) Offer Family Wellness Picnics and site- based activities so parents/caregivers f) The Centralized Enrollment Unit will implement a 5-question screener with all potential enrollees to aid in determining family’s level of need for support. Any results that indicate risk factors will be referred to the trauma specialist. g) Create Parent Cafes around protective factors that improve resilience and strengthen supports within families by providing a safe space for caregivers to share experiences and receive support, including referrals and resources within the community. 45 2 | Page Contra Costa County Employment and Human Services Department ACF-OHS-IM-24-01 Report, CSB Mental Health Unit 9-12-2024 Michelle Mankewich and Jacquline Lopez Padilla and their children can learn and play together. (See Attachment 3) e) Virtual reflective spaces are provided through The California Infant and Early Childhood Mental Health Consultants (IECMHC) Network for caregivers to share experiences, stressors and explore strengths. (See Attachment 4) STRATEGY 2. To promote family well-being, programs must collaborate with parents by providing mental health education support services, including opportunities for parents to learn about healthy pregnancy and postpartum care that encompasses mental health and substance use treatment options (45 CFR §1302.46(a)). a) Offer trainings such as Making Parenting a Pleasure and Parent Resiliency training. (See Attachment 5) b) Devote time in site-based parent meetings and at the Head Start Policy Council for wellness activities. (See Attachment 2) c) All Comprehensive Service staff and Site Supervisor participate in the Family Development Credential Program. d) Create peer support groups. e) Provide resources on reducing stress and understanding depression via Friday Flyers once per month. f) Enhance opportunities for parents to learn about Healthy pregnancy, postpartum care, mental health and substance abuse treatment options are provided through the comprehensive service team and via Friday Flyers. g) Family support groups offered through Partnership with Early Childhood Mental Health Program. STRATEGY 3. To promote staff well-being, programs must make mental health and wellness information available to staff regarding issues that may affect their job performance and must provide staff with regularly scheduled opportunities to learn about mental health, wellness, and health education (45 CFR §1302.93(b)). a) 67 management staff trained and implement Reflective Supervision practices. b) Promote Employee Assistance Program regularly. (See Attachment 6) c) Staff wellness activities integrated into all staff meetings. (See Attachment 7) d) Hosted staff Wellness Summit May 8, 2024. (See Attachment 8) g) Pending approval, the 9 members of the Wellness Team will be certified in Trauma Informed Care. h) Creation of a Social Media campaign aimed at destigmatizing mental health. i) Create a space for staff to have regular check- ins to share, reflect and debrief using a self-care, wellness techniques among co-workers. j) Create a group (processing group) led by outside provider/ contractor to support 46 3 | Page Contra Costa County Employment and Human Services Department ACF-OHS-IM-24-01 Report, CSB Mental Health Unit 9-12-2024 Michelle Mankewich and Jacquline Lopez Padilla e) Provide weekly emails to staff with motivational and wellness tips/ strategies.( See Attachment 9) f) Regularly scheduled staff wellness days- 2 hours quarterly, focused on opportunities for staff to learn about wellness and social emotional well- being. (See Attachment 10) staff by creating a safe space for employees to share and discuss work related issues/ challenges. Space will allow staff to be guided in processing through challenges and issues using strategies/ techniques and support in navigating resources and programs when needed. k) Have staff in all levels trained in Mental Health First Aid to recognize, assist and support co-workers and others who may be experiencing a non-crisis, crisis mental health or substance use challenge. l) Create an online resources library of current tools and services accessible for staff, promoting wellness, Mental Health and health education throughout the county. m) Conduct staff satisfaction survey STRATEGY 4. A program must ensure staff, consultants, contractors, and volunteers implement positive strategies to support children’s well-being and prevent and address challenging behavior (45 CFR §1302.90(c)(i)). a) Use of Teaching Pyramid strategies ensures a culture of positive supports for children’s wellbeing. (See Attachment 11) b) Learning environments are designed to ensure clear supervision with cozy space options following. (See Attachment 12) c) CSB staff are trained to follow the child’s lead. (See Attachment 13) f) Provide every classroom with the Teaching Pyramid Kits to ensure staff have the tools and strategies needed to provide positive supports and address challenging behaviors. g) Provide parents with teaching pyramid parent component resources, training and tool kits to implement positive strategies at home. 47 4 | Page Contra Costa County Employment and Human Services Department ACF-OHS-IM-24-01 Report, CSB Mental Health Unit 9-12-2024 Michelle Mankewich and Jacquline Lopez Padilla d) Parent Teachers conferences are utilized to discuss each child’s unique developmental needs and strategies to support growth. (See Attachment 14) e) Staff use a multi-tier system of supports and best child guidance practices when addressing and preventing challenging behaviors. (See Attachment 15) INCREASE PREVENTION SERVICES AND SUPPORTS STRATEGIES TO INCREASE PREVENTION SERVICES AND SUPPORTS WHAT CSB IS CURRENTLY DOING WHAT CSB IS ADDING STRATEGY 5. To support children’s ongoing social and emotional development, programs must provide supports for effective classroom management and positive learning environments; supportive teacher practices; and strategies for supporting children with challenging behaviors and other social, emotional, and mental health concerns (45 CFR §1302.45(a)). a) Mental Health Consultant from ECMHP collaborate with our Education Team monthly. (See Attachment 16) b) Mental health Consultants from ECMHP provide monthly support groups for our Site Supervisors to discuss classroom needs. c) Teachers complete Ages and Stages Questionnaire (ASQ) within 45 days of enrollment. (See Attachment 17) d) Use of Second Step curriculum to support social emotional learning in the classroom. (See Attachment 18) e) Develop systems to share ASQ data with ECMHP and school districts as needed. (No cost) f) Develop a tap-in/tap-out system for staff when they are feeling overwhelmed, frustrated or dysregulated by hiring floater staff to support staff when concerns arise. g) Hire Behavioral Health Therapist/ABA to support children with challenging behaviors and other needs. h) Create a multidisciplinary team approach with different content area experts (coached, education, mental Health disabilities, etc.) to develop systems to address and support children’s mental health needs and challenging behaviors / other concerns. 48 5 | Page Contra Costa County Employment and Human Services Department ACF-OHS-IM-24-01 Report, CSB Mental Health Unit 9-12-2024 Michelle Mankewich and Jacquline Lopez Padilla i) Train staff in the Use of Lego Education Curriculum to support children in positive learning, social emotional, increase physical skills, cognitive, socialization, language and many other skills. STRATEGY 6. Infant and early childhood mental health consultation (IECMHC) is a prevention- based approach. Mental health consultants work with Head Start leaders, staff, and families to support children’s healthy social and emotional development. Grant recipients have shared that it can be challenging to obtain mental health consultants, particularly in rural areas. a) In collaboration with Contra Costa Behavioral Health, CSB partners with Early Childhood Mental Health Program (ECMHP) to serve children with Medi- Cal. (See Attachment 19) b) Through The California Infant and Early Childhood Mental Health Consultation (IECMHC) Network staff, families and caregivers can seek consultation and participate in reflective spaces. (See Attachment 20) c) Encourage and implement telehealth consultation while services are in place for children, families and staff. STRATEGY 7. To ensure mental health consultants engage in prevention-focused activities, programs must ensure the mental health consultant assists, at a minimum, with the requirements listed in 45 CFR §1302.45(b). a) CSB participates in the Early Childhood Prevention and Intervention Coalition (ECPIC) to advocate for Mental Health needs in the community, alongside First Five, CC Behavioral Health, We Care, ECMHP, C.O.P.E, CoCoKids, and Vistability which in turns allows for community partnerships. (See Attachment 21) b) Partner with Contra Costa Behavioral Health department to ensure children with Medi-Cal have access to direct services through Early Childhood Mental Health Program. ECMHP may refer c) Ensure that Mental Health Consultants attend National and State Head Start Conference to keep up on best practices in the Head Start setting. 49 6 | Page Contra Costa County Employment and Human Services Department ACF-OHS-IM-24-01 Report, CSB Mental Health Unit 9-12-2024 Michelle Mankewich and Jacquline Lopez Padilla children to other providers as needed due to location. (See Attachment 19) ACCESS TO MENTAL HEALTH TREATMENT STRATEGIES FOR ACCESS TO MENTAL HEALTH TREATMENT WHAT CSB IS CURRENTLY DOING WHAT CSB IS ADDING STRATEGY 8. Programs must build community partnerships to facilitate access to additional mental health services as needed (45 CFR §§1302.45(a)(4), 1302.53(a)(2), 1302.80(c)). a) CSB participates in the Early Childhood Prevention and Intervention Coalition (ECPIC) to advocate for Mental Health needs in the community, alongside First Five, CC Behavioral Health, We Care, ECMHP, C.O.P.E, CoCoKids, and Vistability which in turns allows for community partnerships. (See Attachment 21) b) Partnership with Contra Costa Behavioral Health department to ensure children with Medi-Cal have access to direct services through Early Childhood Mental Health Program. ECMHP may refer children to other providers as needed due to location. (See Attachment 19) c) New contract with Early Childhood Mental Health ensures that all children will have direct services regardless of insurance coverage. (See Attachment 22) d) Hire a trauma response expert to enhance direct support services to staff and families. e) Create system to access mental health treatment for all parents, caregivers and staff onsite. f) Build relationships with local colleges and institutions to access services in Mental Health training clinics. g) Create partnerships with community organizations/other counties to access Mental Health First Aid training for staff. 50 1 ACF Administration for Children and Families U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES 1. Log No: ACF-OHS-IM-24-01 2. Issuance Date: 05/09/2024 3. Originating Office: Office of Head Start 4. Key Words: Head Start, Early Head Start, Mental Health, Behavioral Health, Social and Emotional Development INFORMATION MEMORANDUM TO: All Head Start Grant Recipients SUBJECT: Strategies and Recommendations for Supporting Mental Health PURPOSE: BACKGROUND: This Information Memorandum (IM) highlights the Head Start Program Performance Standards and related strategies for integrating mental health supports across all Head Start programs. Head Start programs, including preschool programs, Early Head Start programs, Migrant and Seasonal programs, and American Indian and Alaska Native programs, have a long history of providing comprehensive services alongside early education services. They support a program-wide culture that promotes children’s mental health and social and emotional well-being. Children’s mental health is foundational for family well-being, children’s overall healthy development, and long-term success.1 In recent years, Head Start programs have called for guidance on how to be more intentional in integrating mental health supports into programs. These calls stem from a reported rise in behavioral and developmental concerns, higher rates of staff turnover, and limited availability of specialized mental health services. This IM provides evidence-informed mental health strategies and associated resources that can help address these challenges as part of a renewed effort across federal early childhood funding agencies to integrate mental health supports into programs. To integrate mental health supports effectively into Head Start programs, it is important to first understand and destigmatize what is meant by “mental health.” Young children’s mental health, often referred to as early childhood mental health (ECMH), is not mental illness. Rather, it is the same as social 1 https://www.acf.hhs.gov/ecd/policy-guidance/dear-colleague-social-emotional-development-and-mental-health 51 2 and emotional development and well-being. It is a child’s capacity to express and regulate emotions, form trusting relationships, explore, and learn — all in the cultural context of family and community. ECMH approaches should support every child’s development of social and emotional skills, in addition to providing specialized supports for the up to 20 percent of children under the age of 5 who experience social and emotional difficulties.2 Strengthening the focus on mental health is particularly appropriate given the Head Start program's mission to serve the most vulnerable children and families and break the cycle of poverty. Individuals living in high-poverty neighborhoods often have less access to high-quality resources and supports compared to individuals living in low-poverty neighborhoods, and are more likely to have worse mental health outcomes as a result.3 Furthermore, Black, Indigenous, and People of Color (BIPOC) families4 and families in remote or rural areas have less access to mental health and substance use services.5 BIPOC families, including families in tribal communities, are disproportionately affected by chronic stress resulting from structural racism and historical trauma, which further narrows access to services they can trust.6 Head Start programs play a vital role in addressing ECMH and reducing disparities in ECMH, because they focus on the whole child as well as partner with families and communities. Family-focused efforts in particular ensure children’s mental health continues to be supported in the long-term, after children transition to kindergarten. Many Head Start programs have already adopted diverse strategies to address ECMH. Programs support family well- being and staff-wellness, which ensures caregivers are well equipped to support ECMH. They directly support the child by strengthening relationships with responsive caregivers, such as parents and early childhood staff, which is the foundation of ECMH. They provide stable, nurturing environments in 2 National Research Council and Institute of Medicine Committee. Preventing mental, emotional, and behavioral disorders among young people: progress and possibilities. Washington, DC: National Academies Press; 2009. Brauner, C. B., & Stephens, C. B. (2006). Estimating the prevalence of early childhood serious emotional/behavioral disorders: Challenges and recommendations. Public health reports, 121(3), 303-310. 3 https://www.acf.hhs.gov/ecd/policy-guidance/dear-colleague-social-emotional-development-and-mental-health 4 Rafla-Yuan, E., Moore, S., Carvente-Martinez, H., Yang, P. Balasuriya, L., Jackson, K., McMickens, C., & Ropbles-Ramamurthy, B. (2022). Striving for equity in community mental health: Opportunities and challenges for integrating care for BIPOC youth. Child and Adolescent Psychiatric Clinics of North America, 31(2), 295-312. 5 Morales, D. A., Barksdale, C. L., & Beckel-Mitchener, A. C. (2020). A call to action to address rural mental health disparities. Journal of clinical and translational science, 4(5), 463-467. 6 Winters M-F. Black Fatigue: How Racism Erodes the Mind, Body, and Spirit. 1st ed. Berrett-Koehler Publishers; 2020. Mental-Health-Facts-for-American-Indian-Alaska-Natives.pdf (psychiatry.org) Gone, J. P., & Trimble, J. E. (2012). American Indian and Alaska Native mental health: Diverse perspectives on enduring disparities. Annual review of clinical psychology, 8, 131-160. 52 3 GUIDANCE: which children can safely learn and practice social and emotional skills, and partner with families to do the same at home. Head Start staff build trusting relationships with families and partner within the community to identify and leverage resources. These steps make it more likely that mental health supports will meet the needs of families and make a difference. Although there are many ways mental health can be supported in Head Start programs, it is important for programs to develop a comprehensive, integrated early childhood mental health approach that promotes child and adult mental health, prevents concerns from developing, and supports early identification and referrals for treatment when needed. Using a continuum7 of mental health supports ensures every child and family receives the appropriate level of care. This continuum includes: I. Mental health promotion – An approach aimed at strengthening positive aspects of mental health and well-being and is focused on setting children and families up for success. II. Prevention services and supports – An approach aimed at reducing the likelihood of future disorders in the general population or for people who are identified as at risk of a disorder. III. Access to mental health treatment – Interventions are delivered to people who continue to be at risk after engaging in prevention services or have been diagnosed with a mental disorder. The Office of Head Start (OHS) continues to strongly encourage grant recipients to use quality improvement funds available to all Head Start, Early Head Start, American Indian and Alaska Native Head Start, Migrant and Seasonal Head Start, and Early Head Start-Child Care Partnership grant recipients to support these strategies and invest in mental health supports across roles and program service areas. Suggestions of allowable uses for quality improvement funds as specified in the Head Start Act can be found in the FY 2023 Head Start Funding Increase Program Instruction. For Head Start State Collaboration Offices and recipients closely working with states, it may be of interest to review related program guidance. Strategies and Recommendations to Support Mental Health These strategies support program quality and describe resources that can help programs comply with applicable Head Start Program Performance Standards. 7 Purgato M, Uphoff E, Singh R, Thapa Pachya A, Abdulmalik J, van Ginneken N (2020). Promotion, prevention and treatment interventions for mental health in low- and middle-income countries through a task-shifting approach. Epidemiology and Psychiatric Sciences 29, e150, 1–8. https:// doi.org/10.1017/S204579602000061X 53 4 INCREASE MENTAL HEALTH PROMOTION 1. A focus on social determinants of health, or the conditions in which individuals are born, grow, live, work, and age, can lead to better mental health outcomes and prevent future mental illness. To promote social conditions that support family well-being, such as family safety, health, and economic stability, programs are encouraged to develop innovative two- generation approaches that leverage community partnerships and address prevalent needs of children and families (45 CFR §1302.50(a–b)). To achieve this, programs can: • Create authentic partnerships with families using the Building Partnerships with Families Series as a guide. Programs can support family mental health and well-being by using the family assessment and partnership process to help families with their biggest life stressors. • Update the program’s intake process with families to include targeted discussions on mental health, such as the families’ perceptions about mental health and addressing potential stigma. Include information on mental health supports in the program, such as mental health consultation services and resources and supports available in the community. • Establish formal and informal check-ins with families with the intent to support family mental health. For example, build in time during parent-teacher conferences to discuss how families are doing, create a drop-box for parents to discreetly communicate their needs to staff, and devote time in every parent meeting to wellness activities. • Invite the mental health consultant to introduce themselves at program events, such as an “Open House” to explain the Head Start program’s commitment to supporting mental health. This is an opportunity to familiarize parents with the mental health services available to them, including the role of the mental health consultant and how consultation is used throughout the program. 2. To promote family well-being, programs must collaborate with parents by providing mental health education support services. These services include opportunities for parents to learn about healthy pregnancy and postpartum care that encompasses mental health and substance use treatment options (45 CFR §1302.46(a)). To achieve this, programs can: • Offer opportunities for families to connect to talk about their child’s development, how they are coping with potential stressors, and what resources they are using. Create a parent group, either virtually or in person, that serves as a space for parents to express their emotions, thoughts, and feelings. For guidance on facilitating this activity, refer to Leading Online Parent Meetings and Groups. • Provide training and opportunities for parents to learn about children’s health, well- being, and mental health (i.e., in person trainings, virtual trainings, resources/handouts, etc.), as well as developing safe, stable, and nurturing relationships and environments. For example: i. Use the mental health consultant to provide group wellness sessions with parents. In these sessions include information on resources in the community and how to access these resources. 54 5 ii. Invite speakers from mental health and substance use agencies to give talks about mental health and substance use. • Regularly check in with families about providing supports for their own mental health and well-being, such as education materials on reducing stress and understanding depression. • For pregnant women and expectant families enrolled in Early Head Start services, include a mental wellness check during the newborn visit that a program must provide to each mother and baby within two weeks after the infant’s birth (45 CFR §1302.80(d)). These mental wellness checks are geared towards the parent or family members caring for the child and can be incorporated into a daily health check. Consider incorporating screenings for adult mental health, including depression, and substance use, with appropriate guidance from a mental health professional. 3. To promote staff well-being, programs must make mental health and wellness information available to staff regarding issues that may affect their job performance and must provide staff with regularly scheduled opportunities to learn about mental health, wellness, and health education (45 CFR §1302.93(b)). To achieve this, programs can: • Implement identified policies, procedures, and strategies to support staff wellness that are informed by program data, such as those described in ACF-IM-HS-21-05 Supporting the Wellness of All Staff in the Head Start Workforce. It is important to gather feedback from staff on their well-being and job satisfaction, as well as wellness strategies, to determine if refinements or improvements are needed. • Provide program leaders with foundational training in supporting workforce mental health such as through the National Child Traumatic Stress Network (NTCSN). The NTCSN offers resources and trainings on a wide range of topics, including strategies to prevent, recognize and address secondary traumatic stress, which may be experienced by Head Start staff caring for children affected by trauma. • Consider establishing communities of practice or reflective supervision groups that help directors and managers focus on creating safe environments and communications that convey to staff that it is safe to disclose and receive support if and when they experience mental health challenges. • Promote employee assistance services and build a culture to address the stigma of seeking help for mental health reasons. Raise employee awareness around free or low-cost mental health supports available, such as benefits included in health insurance plans. 4. To promote child well-being, a program must ensure staff, consultants, contractors, and volunteers implement positive strategies to support children’s well-being (45 CFR §1302.90(c)(i)). To facilitate implementation of positive strategies, programs can: • Train staff, consultants, contractors, and volunteers to have basic knowledge of developmentally appropriate strategies to support positive behaviors. Since developmental expectations and appropriate strategies may differ depending on a child’s age and developmental skills, staff working with preschool-age children, may still benefit from a basic understanding of how to support infants and toddlers. 55 6 • Ensure staff understand that following children’s lead in structured play activities is an impactful way to understand children’s developmental skills, identify and offer positive attention to their strengths, and practice self-regulation skills in a controlled environment. • Make sure learning environments are designed to support children’s self-regulation. This could include creating “cozy spaces” that are clearly visible to adult supervision where children can go if they are feeling overwhelmed. Similarly, spaces can be created with activities or sensory materials as places to express energy. These types of spaces are designed so that teachers can still observe the child or children who are in them, while also providing them the needed supports to self-regulate. • Partner with families to understand the development, communication style, strengths, and temperament of each child in order to establish predictable routines, transition strategies, and developmentally appropriate behavioral expectations for children in the program. INCREASE PREVENTION SERVICES AND SUPPORTS 5. To support children’s ongoing social and emotional development, programs must provide supports for effective classroom management and positive learning environments; supportive teacher practices; and strategies for supporting children with challenging behaviors and other social, emotional, and mental health concerns (45 CFR §1302.45(a)). To achieve this, programs can: • Implement an all-hands-on deck approach by creating a multidisciplinary team that works together in your program to support children’s mental health. This team can be comprised of individuals that already work with the child or family across disciplines. The benefit of having a team of professionals with multiple perspectives (i.e., mental health, early childhood, special education, family service, health, nutrition, etc.) is that it ensures the most comprehensive approach to support the needs of a child and family. i. For example, consider ways to integrate prevention-focused approaches such as the Pyramid Model with mental health supports such as mental health consultation. • Seek direct guidance from a mental health or child development professional to ensure that findings from developmental screening and assessment required in 45 CFR §1302.33, including social and emotional screenings, are used when making a referral to determine if the child is eligible for services through IDEA or section 504 of the Rehabilitation Act. While programs wait for an eligibility evaluation and possible services, programs can consider an individualized approach to support positive behaviors and teach new skills. • Review your program’s educational curriculum to ensure it offers appropriate social and emotional learning opportunities, including intentionally planned learning experiences to help practice self-regulation skills. If you notice that many children in the group need social and emotional development support, spend some time intentionally embedding more of the experiences and activities from your curriculum that support these skills. Work these activities and supports into your daily routines and revisit them as needed to ensure children are developing skills in this area. 56 7 • Implement a systems-level approach for adult regulation such as the “Tap-In/Tap- Out” system8 when an education staff member is feeling frustrated, overwhelmed, or otherwise dysregulated. This strategy allows for education staff to engage another staff member from a predetermined list to “tap-in” and cover the class. The education staff member can then “tap-out” and engage in strategies for accessing a calm state before returning to the learning environment. 6. Infant and early childhood mental health consultation (IECMHC) is a prevention-based approach. Mental health consultants work with Head Start leaders, staff, and families to support children’s healthy social and emotional development. Grant recipients have shared that it can be challenging to obtain mental health consultants, particularly in rural areas. A few strategies for building mental health consultation access include: • Encourage existing staff to use educational benefits, such as tuition and fee support, to work towards mental health consultant qualifications. These and other strategies are described in ACF-IM-HS-22-06 Strategies to Stabilize the Head Start Workforce. • Reach out to mental health organizations and other early childhood programs to identify potential partners for mental health consultation services. For example, ask other local Head Start or early childhood programs, home visiting programs, and state or tribal early care and education offices how they find mental health consultants. Ask local pediatricians, community health clinics, and hospitals where they refer children and adults for mental health services. After identifying possible partners, reach out to orient them to the role of mental health consultation in Head Start programs and explore potential collaborations. • Prioritize finding a mental health professional who is familiar with the families in your program or community. Your Head Start program can help them learn about child development, group care, the culture of your program, relevant HSPPS, and IECMHC. • Consider implementing approaches such as telehealth or remote consultation, especially in rural areas, while efforts to build capacity for in-person mental health consultation are underway.9 • Consult IECMHC.org’s interactive map of consultants. 7. To ensure mental health consultants engage in prevention-focused activities, programs must ensure the mental health consultant assists, at a minimum, with the requirements listed in 45 CFR §1302.45(b). To achieve this, programs can: • Provide professional development opportunities for staff during onboarding and periodically after. For example, the Foundations of Infant and Early Childhood 8 Venet, A. S. (2019, September 13). The evolution of a trauma-informed school. Edutopia. 9 Mental Health Services for Children Policy Brief | CSELS | Rural Health | CDC Terry-Leonard et al (2022). Early childhood mental health consultation: Brief report of adaptations in the virtual learning environment. ECMHCvirtualAdaptations_v6.pdf (iecmhc.org) 57 8 Mental Health Consultation iPD Course can ensure that all staff understand that IECMHC is a way to grow the capacity of adults to support the child’s social and emotional well-being, rather than a direct intervention or treatment approach. • Use the expertise of the mental health consultant at a programmatic level, in addition to consulting at the level of specific children, families and classrooms. For example, the mental health consultant can help program leaders and staff explore strategies for enhancing systems that support staff well-being. They can also help design program- wide policies and procedures related to mental health supports, such as positive discipline or screening and assessment practices. ACCESS TO MENTAL HEALTH TREATMENT 8. Programs must build community partnerships to facilitate access to additional mental health services as needed (45 CFR §§1302.45(a)(4), 1302.53(a)(2), 1302.80(c)). To achieve this, programs can: • Consult with your Health Services Advisory Committee on local opportunities and potential partnerships. Identify grant funds available in your local community that are designated to support early childhood mental health. For example: i. Partner with local Certified Community Behavioral Health Clinics (CCBHCs). CCBHCs are designed to ensure access to coordinated comprehensive behavioral health care. CCBHCs are required to serve anyone who requests care for mental health or substance use, including developmentally appropriate care for children and youth, regardless of their ability to pay, place of residence, or age. ii. Leverage community health workers, family navigators, promotores, and peer specialists to reduce mental health stigma and provide support to families navigating mental health systems and other systems that address social determinants of health. • Use resources that offer expertise in culturally grounded mental health practices, such as partnering with tribal healers to connect families to traditional ways of healing. • Build partnerships with local colleges and universities that may provide sliding scale mental health services through their mental health training clinics. A sliding scale is a flexible fee structure or payment system that asks a client to pay based on their ability to do so. • Facilitate access to community enrichment activities that can both protect and promote child and family mental health (i.e., sporting activities, cultural events, religious organizations, farmers’ markets, and play groups). • Assess barriers to obtaining mental health services and provide supports based on this assessment to facilitate access. Examples could include providing transportation from the program to clinics or providing families with private spaces equipped with appropriate technology to access tele-mental health services. These evidence-informed mental health strategies can support Head Start programs in intentionally integrating mental health supports across their program. They can address challenges programs face such as behavioral and developmental concerns, staff burnout, and the 58 9 limited availability of specialized mental health services. The accompanying appendix includes more specific resources to support these recommendations. OHS will continue to work with programs to support the mental health of children, families, and staff in Head Start programs. Please direct any questions about the content of this IM to your OHS regional office. Thank you for all you do on behalf of children and families. Sincerely, / Khari M. Garvin/ Khari M. Garvin Director Office of Head Start 59 10 APPENDIX: RESOURCES TO SUPPORT RECOMMENDATIONS The Appendix includes a variety of resources that support the promotion of mental health and well-being of children, families, and the child care workforce. Examples include different funding streams and supports from initiatives, programs, and agencies that support early childhood development and family well-being. The Appendix is by no means exhaustive but includes examples of best and promising practices that are research- and/or evidence-based. Specific mention of organizations does not imply endorsement by ACF, HHS, or the U.S. government. MENTAL HEALTH PROMOTION STRATEGY 1. A focus on social determinants of health, or the conditions in which individuals are born, grow, live, work and age, can lead to better mental health outcomes and prevent future mental illness. To promote social conditions that support family well-being, such as family safety, health, and economic stability, programs are encouraged to develop innovative two- generation approaches that leverage community partnerships and address prevalent needs of children and families (45 CFR §1302.50(a–b)). • Resources on the ECLKC to support programs in understanding and addressing broader social conditions and events that impact mental health include: o The Mental Health and Wellness chapter of the Health Manager Orientation Guide describes the importance of social determinants of health and equity as it relates to mental health. o The Head Start Heals Campaign is a collection of resources on the ECLKC describing how to support the mental health of children and families, particularly when children and families are exposed to traumatic events or situations that overwhelm their ability to cope. o Family Support and Well-being is a collection of resources on the ECLKC for ensuring family members are safe, healthy, and have chances for educational advancement and economic mobility. o Building Partnerships with Families is a four-module learning series to enhance knowledge and practice about engaging families using strengths-based attitudes, relationship-based practices, and reflective practice. This professional development course accessible for free on the Individualized Professional Development Portfolio with continuing education units awarded for completion. o Family Engagement and Cultural Perspectives: Applying Strengths-based Attitudes tool, can be used as part of training and reflective practice and supervision. o Check In and Partner with Families offers relationship-based competencies to support family engagement, recognizing that partnering with families supports child and family well-being. o Challenges and Benefits of Making Parent Connections provides strategies for connecting with parents. • Resources on the ECLKC to support partnerships with families around mental health include: 60 11 o Family Connections: A Mental Health Consultation Model provides preventative, systemwide mental health consultation and training approach for staff. These resources and training modules support staff to work with families dealing with parental depression and related adversities, with children in classrooms and in the home, and to engage and support parents struggling with adversities. o Infant and Early Childhood Mental Health Consultation: Information for Families provides an overview of mental health consultation for families. o Leveraging Sources of Resilience to Support Mental Health webinar discusses the importance of finding, understanding, and elevating sources of resilience to support mental health, with a focus on racially and ethnically diverse and under- resourced communities. • Other resources to support family relationships and partnerships include: o Information for Caregivers on Infant and Early Childhood Mental Health Consultation is a one-page resource to help caregivers learn about the benefits of infant & early childhood mental health consultation. o ACF Video Series on Early Childhood Social Emotional Development and Mental Health and Caregiver Well-being is a series of short videos spotlighting the importance of robust social emotional development and mental health support strategies within programs serving young children and their families. • Potential partnership opportunities for Head Start programs include: o Healthy Start programs are Health Services and Resources Administration (HRSA) grant recipients situated in many communities and can work as partners with Head Start programs. Healthy Start programs seek to improve health outcomes before, during, and after pregnancy. Local Healthy Start programs match families with a care coordinator, who then develops personalized plans that can include prenatal and post-partum care, mental health and substance use screening, intimate partner violence screening, and linkages to other services such as assistance with transportation and housing. Every Healthy Start project also has a Healthy Start Community Action Network to increase awareness of and partnerships with a wide range of programs offering health, behavioral health, and social supports. As of 2023, there were 111 Healthy Start projects; some Healthy Start grant recipients already collaborate with Head Start programs. STRATEGY 2. To promote family well-being, programs must collaborate with parents by providing mental health education support services, including opportunities for parents to learn about healthy pregnancy and postpartum care that encompasses mental health and substance use treatment options (45 CFR §1302.46(a)). • Resources on the ECLKC that support families during pregnancy, infancy, and the transition to parenthood can be found in the Pregnancy collection, including: o The Newborn Visit: Information for Early Head Start Staff describes and provides tips for the newborn visit. o Head Start Services as a Maternal Health Intervention webinar includes information on maternal depression and conversations on health equity in maternal health. o Connecting All Parents with Perinatal Mental Health Services webinar addresses the unique needs of specific birthing people — such as LGBTQI+ people, 61 12 indigenous people, immigrants, and refugees — who may benefit from specialized or tailored mental health resources during and after pregnancy. o These resources support screenings for depression and substance use. • Other resources to promote healthy pregnancy and postpartum care and support families experiencing perinatal mental health challenges include: o The Perinatal Mental Health page provides basic information on perinatal mental health and links to a wide range of resources, webinars, and free trainings. It is developed by the Substance Abuse and Mental Health Services Administration (SAMHSA)’s Mental Health Technology Transfer Center Network. SAMHSA also has a webpage with Resources for Parents and Caregivers. o The Mom’s Mental Health Matters Initiative provides extensive information about depression and anxiety during pregnancy and postpartum, including signs and symptoms, risk factors, and treatment options. It is developed by the National Institute of Child Health and Human Development (NICHD) at the National Institutes of Health (NIH). They have materials (such as posters and postcards) that can be ordered and disseminated by Head Start programs. o The Action Plan for Depression and Anxiety Around Pregnancy serves as a checklist to help identify and seek help for anxiety and depression from the NIH. o Depression During and After Pregnancy provides information about perinatal depression and links to find effective treatment and community resources such as Postpartum Support International, the National Suicide Prevention Lifeline, and the National Alliance on Mental Illness. It is developed by the Centers for Disease Control and Prevention (CDC). • Resources on the ECLKC that support family engagement activities include: o Talking with Families about Their Child’s Development provides strategies to partner with families in ongoing conversations about growth and development. o Leading Online Parent Meetings and Groups resource offers examples to consider before, during, or after leading online parent activities. o Family Engagement in Early Care and Education Learning Series modules guide early childhood professionals to consider how the relationships they build with families can support positive parent-child relationships, learn how to use reflective practice as one strategy to enhance work with families, and explore how larger systems and cultural contexts influence family engagement. This resource includes modules on understanding children’s behavior as communication and responding with families to developmental concerns. o Partnering with Families to Support Inclusion: Part 1 webinar offer strategies that program staff can use to support families to learn about and act on developmental concerns. o Supporting Social and Emotional Well-being is a collection of resources that can inform professional and parental development. • Other resources to help families understand their child’s development and mental health include: o Essentials for Parenting Toddlers and Preschoolers is an online resource for parents of 2- to 4-year-olds which provides information on positive parenting strategies. The website includes articles and FAQs answered by parenting experts, videos, and free print resources developed by the CDC. 62 13 o “Learn the Signs. Act Early.” is a CDC initiative that provides free materials and resources to help families and early childhood professionals promote developmental monitoring and screening activities, track developmental milestones, and recognize signs of developmental concerns. With family-friendly resources available in print, online, and via CDC’s Milestone Tracker App, information can help families and professionals learn the signs of social- emotional development and encourage them to act early to access screening and additional services when they have any questions or concerns. o The aRPy Ambassador Program identifies individuals who can help Head Start programs and families implement the Division of Early Childhood (DEC) Recommended Practices: a set of research-based best practices for working with young children with disabilities or delays, their families, and the personnel who serve them. The program is co-led by the Early Childhood Technical Assistance Center (ECTA) through a Department of Education Office of Special Education Programs cooperative agreement. o Healthy Steps: Healthy Steps Should I be concerned? Understanding and talking about mental health with your child is a video about parenting and signs of mental health concerns in children. It features parents and caregivers from around the country who talk about how they noticed and responded to their child’s mental health concerns. This video highlights federal resources about mental health and where to get help. o Talking about mental health: Tips for parents and caregivers from young people is a tip sheet created by young people who have experienced mental health challenges. Youth share what has helped and what they wish parents and caregivers would say and do when talking about mental health. The tip sheet also includes links to additional resources. ACF also has a webpage dedicated to Mental Health Resources for Parents and Caregivers. • Potential partnership opportunities for Head Start programs to promote healthy child development and mental health include: o Healthy Steps Specialists in pediatric primary care practices offer developmental, social-emotional, and behavioral screening for all young children (birth to 3), screening for family needs, care coordination, parenting support, and consultation for children and families. Where applicable, Head Start programs can partner with Healthy Steps sites in their communities to coordinate care for families. There are currently Healthy Steps sites in 24 states and the District of Columbia, and more than 200 primary care practices. • Resources on the ECLKC to support a family’s own mental health include: o Several materials designed for use with families, including materials on reducing stress, understanding depression, taking care of yourself, and coping with grief and loss. o Fathers, Families, and Mental Health is a webinar that explores how to best support the family system by learning about the unique experiences of fathers, appropriate screening tools and interventions, and the impact of the father on the family. 63 14 o Understanding Addiction and Substance Use Stigma: What You can Do to Help provides information on substance use disorders and how to support those impacted by substance use. o Strategies to Support Families Who May Be Experiencing Domestic Violence provides resources for staff working with families who may be experiencing intimate partner violence. o Should I be concerned? Understanding and talking about mental health with your child o Talking about mental health: Tips for parents and caregivers from young people o Mental Health Resources for Parents and Caregivers. • Various helplines have been developed to provide free and direct mental health support to individuals, including staff and families: o HRSA funds the National Maternal Mental Health Hotline which provides free and confidential support (in English and Spanish) before, during, and after pregnancy. o SAMHSA has a number of national helplines and free resources to help individuals access behavioral health treatment that can be made available to families. These include: ▪ Findtreatment.gov offers a confidential and anonymous resource for persons seeking treatment for mental and substance use disorders in the United States and its territories. ▪ 988 Suicide and Crisis Lifeline offers free and confidential support for people in distress, 24/7. ▪ National Helpline offers treatment referral and information ▪ Disaster Distress Helpline offers immediate crisis counseling related to disasters, 24/7. ▪ Programs can order free printed posters and other materials from SAMHSA's store. o Stronghearts Native Helpline 1-844-7NATIVE (762-8483) is a safe, anonymous, and confidential domestic and sexual violence helpline for Native Americans and Alaska Natives, offering culturally appropriate support and advocacy. o The Native Crisis Text Line connects those seeking crisis support with a trained counselor by texting the word “NATIVE” to 741741. STRATEGY 3. To promote staff well-being, programs must make mental health and wellness information available to staff regarding issues that may affect their job performance and must provide staff with regularly scheduled opportunities to learn about mental health, wellness, and health education (45 CFR §1302.93(b)). • Promoting Staff Well-being is a collection of resources on the ECLKC website to support staff wellness and mental health, including: o Cultivating Wellness: 8 Dimensions of Staff Well-being offers early childhood program staff strategies to cultivate their health and well-being. This professional development course accessible for free on the Individualized Professional Development Portfolio with continuing education units awarded for completion. o Managing Stress with Mindful Moments offers resources such as breathing and movement exercises. 64 15 o You Make the Difference Posters can be displayed to help staff find inspiration and practical strategies to reduce stress. o Promoting Organizational Staff Wellness webinar explores how to build an organizational and program-wide culture of wellness. o Practical Strategies for Improving Staff Wellness webinar discusses practical strategies for nurturing staff’s well-being and hear about ideas and resources to build wellness into their everyday routines. o Tips to Support Family Services Staff Wellness is a resource that offers program strategies for leaders and supervisors and self-care tips for family services professionals and home visitors. o Staff Wellness for Home Visitors webinar explores the importance of staff wellness and professional boundaries in home-based settings. o Strengthening Trauma-Informed Staff Practices brief outlines different strategies to strengthen trauma-informed practices for staff. o Promoting Healing and Resilience with Staff and Families webinar offers ideas and strategies for creating trauma-informed and healing-centered interactions before, during, and after traumatic events. • Resources on the ECLKC that focus on ensuring workforce job satisfaction and engagement include: o Improving Head Start Workforce Compensation, Wellness, and Career Advancement, Office of Head Start staff discussed strategies related to compensation, benefits, and well-being. o Improving Staff Wellness and Job Satisfaction webinar explores meaningful self- care strategies that improve wellness and job satisfaction and help staff perform their job with resilience. o Using Brain Science to Inspire and Motivate Education Staff webinar explores how to create and sustain motivation and commitment to high-quality service, even when the work is challenging and at times stressful. o Tips on Becoming a Reflective Supervisor and a Reflective Supervisee includes information sheets to support the workforce in using reflective supervision practices. o Using Reflective Supervision to Build Capacity webinar outlines information for supervisors and staff on how reflective supervision can be used to build reflective capacity for education staff and improve program quality and practice. • Other resources to support Head Start workforce well-being and mental health include: o Psychological First Aid resources are designed to reduce the initial distress caused by traumatic events and to foster short- and long-term adaptive functioning and coping. Psychological First Aid is developed by The National Child Traumatic Stress Network and National Center for PTSD. o Infant/Toddler Workforce Wellness: Focusing on Wellness is Critical for Early Childhood Professionals offers resources for child care providers looking to reduce stress and prioritize their own wellness, curated by Office of Child Care. o Mental Health and Wellness Resources contains resources for child care providers to support both their own mental health and the mental health of the children they serve curated by the Office of Child Care. 65 16 • SAMHSA’s National Child Traumatic Stress Initiative (NCTSI) raises awareness about the impact of trauma on children and adolescents. Through this initiative, the National Child Traumatic Stress Network (NCTSN) offers resources and trainings on a wide range of topics, including strategies to prevent, recognize and address secondary traumatic stress, which may be experienced by early childhood providers caring for children affected by trauma. o Secondary Traumatic Stress: A Fact Sheet for Child-Serving Professionals, from NCTSN For example, Secondary Traumatic Stress: A Fact Sheet for Child- Serving Professionals, describes how individuals experience secondary traumatic stress (STS), how to identify STS, and strategies for prevention and intervention. NCTSN was created through SAMHSA’s National Child Traumatic Stress Initiative. o Trauma-Informed Care for Schools Before, During, and After Possible Emergency Events resources are created by the Department of Education’s Readiness and Emergency Management for Schools (REMS) Technical Assistance Center. o Understanding Educator Resilience and Developing a Self-Care Plan is a webinar which provides educators with information on the concepts of resilience and compassion fatigue, and the impact of stress and burnout on the education environment, as well as ways to identify signs and symptoms of compassion fatigue and concrete steps for developing a professional self-care plan. It was developed by the Department of Education’s Readiness and Emergency Management for Schools Technical Assistance Center. o Total Worker Health®: A Guide to Worksite Wellness and Safety in the Child Care Setting is a comprehensive toolkit based on CDC evidence for child care center leaders and staff to learn safe and healthy skills for themselves and learn how to be healthy role models for the children they see every day. o Supporting Mental Health in the Workplace is a CDC/NIOSH Science blog that discusses workplace strategies to support worker mental health and well-being and organizational success. STRATEGY 4. A program must ensure staff, consultants, contractors, and volunteers implement positive strategies to support children’s well-being and prevent and address challenging behavior (45 CFR §1302.90(c)(i)). • Resources on the ECLKC on positive strategies to support children’s behaviors include: o Infant/Toddler Positive Behavior Support and Preschool Positive Behavior Support from the Pyramid Model Framework are webinars from the Teacher Time series focused on building relationships, emotional literacy, problem- solving and relationship skills, responding to persistent challenging behavior, and more. o Engaging Interactions and Learning Environments in-service suites are a professional development resource that include several resources for social and emotional support, well-organized classrooms, and instructional interactions. Several suites have additional materials that have been specifically designed for programs with American Indian and Alaska Native populations. 66 17 o Following Children’s Lead is a webinar on social and emotionally intelligent ways in which teachers can engage children in learning. o Understanding and Managing Children’s Behavior Tip sheet offers information on supporting children who need more help managing strong emotions by developing and using an Individual Support Plan (ISP). o Mindfulness Practices with Children provides audio recordings of mindfulness practices with the Sesame Street Muppets. • Resources on the ECLKC to help families understand child development include: o Introduction to Temperament is an ECLKC resource providing an overview of what temperament is, including the nine common traits that can help to describe a child’s temperament and how they react to and experience the world. This form can be used by families to help understand where their child falls on the Temperament Continuum. o Positive Solution for Families: Routine Guide is a resource for families of children 2-5 years old. It offers suggestions and strategies to prevent, teach, and respond, to the challenging behavior a child may be having. • The National Center on Pyramid Model Innovation’s resource library includes several resources on positive behavior supports, such as: o Pyramid Model Practices Implementation Checklist for Preschool (2-5 years) Classrooms this checklist highlights high quality practices to support nurturing and responsive relationships; high quality, supportive environments; teaching social-emotional skills; and addressing challenging behavior. o Taking a Break: Using a Calm Down Area at Home resource to support families in creating environments that support a child’s self-regulation at home. o Help Us Calm Down: Strategies for Children visual support that can be used in learning settings. • Other programs that offer resources to support parenting and help families understand and promote their child’s development include: o Introduction to Temperament is an ECLKC resource providing an overview of what temperament is, including the nine common traits that can help to describe a child’s temperament and how they react to and experience the world. This form can be used by families to help understand where their child falls on the Temperament Continuum. o Positive Solution for Families: Routine Guide is a resource for families of children 2-5 years old. Parent Training and Information Centers (PTIs) serve families of children (birth to 26) and inclusive of all disabilities. These centers provide training and information that meets the needs of families of children with disabilities. o Community Parent Resource Centers (CPRCs) are parent training and information centers operated by local parent organizations that help ensure underserved families of children with disabilities (including low-income families, parents of children who are English learners, and parents with disabilities) have the training and information they need to participate effectively in helping their children. CPRCs are required to establish cooperative partnerships with the parent training and information centers in their states. 67 18 o Parent Cafes: Many communities have implemented parent cafes with funding and other supports from state or local health and mental health departments, grants from SAMHSA (Project LAUNCH), or family resource centers and other community organizations. Learn more in the March 2020 Children’s Bureau brief on approaches to strengthening protective factors in child welfare. o The Grandfamilies & Kinship Support Network offers free technical assistance and resources to government agencies and nonprofit organizations in states, tribes, and territories to improve supports and services for grandfamilies and kinship families. For example, this tip sheet discusses starting grandfamily support groups. The network is funded through the Administration for Community Living (ACL). o Thriving and Healthy Kids: We All Have a Role to Play in Promoting Positive Childhood Experiences is a resource website created by ACF and CDC in partnership with parent leaders and the American Academy of Pediatrics and Prevent Child Abuse America. The resources were developed to help individuals learn more about how they can use existing strategies and resources to play a role in preventing adversity and promoting positive experiences so children can thrive. PREVENTION SERVICES AND SUPPORTS STRATEGY 5. To support children’s ongoing social and emotional development, programs must provide supports for effective classroom management and positive learning environments; supportive teacher practices; and strategies for supporting children with challenging behaviors and other social, emotional, and mental health concerns (45 CFR §1302.45(a)). • Resources on the ECLKC to support multidisciplinary team approaches include: o All Hands-on Deck: Partnering with Infant and Early Childhood Mental Health (IECMH) Consultants to Implement the Pyramid Model is a resource from the National Center for Pyramid Model Innovations and highlights different ways an IECMH consultant can directly support Pyramid Model implementation. o The Crosswalk of Infant Early Childhood Mental Health Consultation and Pyramid Model Coaching: Building Capacity in Early Childhood for the Promotion of Social and Emotional Health supports visualization of the unique and complimentary aspects of IECMH consultation and the Pyramid Model. • Resources on the ECLKC on individualizing approaches for children, include: o IDEA resource collection offers information related to the federal law that guarantees early intervention and early childhood special education services for children with disabilities from birth to age 5. o Section 504 of the Rehabilitation Act is a federal statute that prohibits discrimination based on disability in certain programs, including those that receive Federal financial assistance. Section 504 requires these programs to provide qualified individuals with disabilities, including preschool-aged children, equal opportunity to participate in the program. Programs that provide preschool education must also take into account the needs of qualified preschool-aged children with disabilities in determining the aids, benefits, or services to be provided. 68 19 o Highly Individualized Practices Series is a webinar series that offers effective strategies for teachers, home visitors, and coaches to use when supporting children with disabilities or suspected delays. o The Inclusion Lab App is a mobile application designed to help disability service coordinators, education managers, and coaches support education staff to provide highly individualized instruction for young children with disabilities or suspected delays. o Understanding and Managing Children’s Behaviors: Individual Support Plans (ISP) this ECLKC resource offers strategies, resources, and a process for developing an ISP. o Developing a Neutralizing Routine is a resource that supports a plan for how to address challenging behavior when it occurs to ensure responses to the behavior does not escalate it and aims to neutralize the effects of implicit bias on decision making. • Social Emotional Learning is a collection of resources on the ECLKC such as webinars and 15-minute in service suites. Social and emotional learning begins with positive relationships, supportive learning environments, actively teaching social emotional skills, and understanding behavior including challenging behavior. STRATEGY 6. Infant and early childhood mental health consultation (IECMHC) is a prevention-based approach. Mental health consultants work with Head Start leaders, staff, and families to support children’s healthy social and emotional development. Grant recipients have shared that it can be challenging to obtain mental health consultants, particularly in rural areas. • Resources on the ECLKC to support programs to access mental health consultants include: o Infant and Early Childhood Mental Health Consultation and Your Program is a resource collection that includes information about how to choose and use an IECMH consultant, how to deliver effective IECMH consultation services. o The ECLKC offers Tips for Offering Effective Mental Health Consultation in Ever-changing Contexts. This resource explores strategies and tips Head Start programs can use to build strong IECMH consultation services, including using technology as a substitute or supplement to in-person services. • Early care and education offices are state or local entities that oversee early care and education programs and services. Programs can reach out to offices to identify potential partners for mental health consultation services. • Resources to help identify mental health consultants developed by the Center of Excellence (CoE) for IECMHC include: o Infant and Early Childhood Mental Health Consultation Hiring Guidance o Infant and Early Childhood Mental Health Consultation Workforce Development Plan Overview o Virtual Community interactive map of consultants who self-identify as infant and early childhood mental health consultants STRATEGY 7. To ensure mental health consultants engage in prevention-focused activities, programs must ensure the mental health consultant assists, at a minimum, with the requirements listed in 45 CFR §1302.45(b). • Resources on the ECLKC on mental health consultation activities include: 69 20 o Foundations of Infant and Early Childhood Mental Health Consultation offers a detailed learning experience for mental health consultants and anyone who currently uses or wants to learn more about Infant and Early Childhood Mental Health Consultation. This professional development course accessible for free on the Individualized Professional Development Portfolio with continuing education units awarded for completion. o The Infant and Early Childhood Mental Health Consultation section of the Health Managers Orientation Guide describes the role, services, and supports of a mental health consultant. • The CoE for IECMHC has several resources to support mental health consultants to engage in prevention-focused activities, tailored to specific needs or early childhood populations, including: o Racial Equity Toolkit is a collection of videos, tools, and resources that can help consultation systems, leaders, and practitioners in building capacity for understanding race and systemic racism, bias, and culturally responsive practices. o Equity in IECMHC: Looking back, looking forward is a webinar that features a panelist of practitioners who are meaningfully advancing the work of equity in IECMHC, including an example how a community developed their own IECMH consultants o Making a Difference: Maternal Depression: This video describes how maternal depression affects infants and toddlers, and how IECMH consultants can build the capacity of home visitors and early care and education staff to address maternal depression. This video includes highlights from a webinar presented on 3/27/18. o Considerations for Providing Infant and Early Childhood Mental Health Consultation in Early Care and Education Settings to Support Children in Foster Care is a brief that explains how infant and early childhood mental health consultation can mitigate the challenges children in foster care face, as well as the challenges that early childhood education teachers and other program staff face in providing the best possible care for them. o Family Engagement: Explore IECMHC Strategies for Enhancing Family Engagement webinar highlights the family engagement framework developed by the National Center on Parent, Family and Community Engagement. The webinar features examples of how IECMH consultants can support enhanced family engagement in early care and education programs. o Beyond the 101: Providing IECMHC to Infant Toddler Caregivers is an e- learning module that explores the needed shifts in thinking and perspective when providing IECMHC in settings serving primarily infants and toddlers. ACCESS TO MENTAL HEALTH TREATMENT STRATEGY 8. Programs must build community partnerships to facilitate access to additional mental health services as needed (45 CFR §§1302.45(a)(4), 1302.53(a)(2), 1302.80(c)) • Resources on the ECLKC to support community engagement include: 70 21 o The Engaging Community Partners to Support Mental Health section of the Health Manager Orientation Guide describes mental health specific considerations for community engagement to support mental health. o Strategies and Examples for Community Partnerships is a resource that outlines how Head Start programs can work with community partners to support positive outcomes for children and families. • Resources on the ECLKC relevant to culturally grounded mental health approaches include: o Mental Health and Equity webinars highlight the importance of understanding, affirming, and supporting nondominant ways of responding to mental health challenges and raise awareness about the effect of historical trauma on mental health and how to reduce barriers of bias. o Head Start Programs, Indigenous Families, and Addiction links to a video series that discusses the most important concepts and facts regarding addiction, explores the experience of many Indigenous people, and uncovers how to make substance use recovery support more responsive. • Resources on the ECLKC that support access to mental health treatment information and referrals: o Finding a Mental Health Provider for Children and Families in Your Early Head Start/Head Start Program offers guidance in identifying mental health providers who best meet a family’s needs, culture, and personality and ideas to overcome barriers. o Facilitating a Referral for Mental Health Services for Children and their Families is a brief that offers Head Start program staff guidance on special considerations for making and supporting successful referrals. • Other resources to support engagement with community mental health partners include: o Certified Community Behavioral Health Clinics (CCBHCs) are designed to ensure access to coordinated comprehensive behavioral health care. This SAMHSA resource outlines the history and background of CCBHCs, offers information about expansion grants and certification criteria, as well as technical assistance and resources. Visit the CCBHC locator page to view an interactive map and downloadable PDF list of CCBHCs by state. o Visión y Compromiso offers information on the roles of promotores and community health workers. o The Find a HRSA Health Center tool provides information about where HRSA- supported health centers are located in each community. These centers provide comprehensive primary care services through permanent, fixed service delivery sites, temporary locations, mobile units, and service delivery sites located in or proximate to schools. Health center school-based service sites help to facilitate access to essential services for students, family members and other members of the community. Find a Health Center provides information about where health centers are located in each community. The Children’s Health and Education Mapping Tool from the School Based Health Alliance enables health, education, and other partners to identify each other at a local level and develop new partnerships. 71 22 o The HHS School-Based Health Services resource list (March 2022) is an expansive compendium of resources for educators grouped topically and including early care and education, emergency response, behavioral health and trauma, social determinants of health, and health care coverage. o Regional Partnership Grants (RPG) are administered by the Administration for Children, Youth, and Families Children’s Bureau (CB) to improve the well-being of children affected by parental substance use disorders. The projects support interagency collaborations and integration of programs, services, and activities designed to increase the well-being, improve the permanency, and enhance the safety of children who are in, or at risk of, out-of-home placements as the result of a parent or caregiver’s substance use disorder. o National Center on Substance Abuse and Child Welfare (NCSACW) provides technical assistance to RPG grantees and community partners on cross-systems collaborative capacity; program sustainability; trauma-informed and culturally responsive evidence-based and evidence-informed services for children, parents, and family members; family-centered substance use and mental health disorder treatment and recovery support services; and lasting systems change. o The Child Welfare Capacity Building Center for States is part of a collaborative funded by the CB at ACF to provide support to state and territorial child welfare agencies and their partners. The Center for States helps agencies to deliver services that are grounded in racial equity, follow evidence-based processes and practices, and keep children, youth, and families safe and thriving. There are 10 Child Welfare Capacity Building Collaborative Liaisons who serve as single points of contact for all Center activities within their regions. o Infant-Toddler Court Program – National Resource Center grants change child welfare practices and improve the early developmental health and well-being of infants, toddlers, and their families by expanding research-based infant toddler court teams. • Resources relevant to providing culturally grounded and responsive mental health services from SAMHSA include: o The Improving Cultural Competence Treatment Intervention Protocol guide helps professional care providers and administrators understand the role of culture in the delivery of mental health and substance use services. It describes cultural competence and discusses racial, ethnic, and cultural considerations. o Racial Equity and Cultural Diversity Resource Collection webpage includes a compilation of products and resources on cultural responsiveness, racial equity, and cultural diversity for the mental health workforce. o Information on IECMHC and Tribal Nations is a web page created to support programs, local governments, and tribal nations in creating better services and systems for their infants, toddlers and young children and their families through Infant and Early Childhood Mental Health Consultation program. 72 Absenteeism in Head Start and Children’s Academic Learning Arya Ansari and University of Virginia Kelly M. Purtell The Ohio State University Abstract Using nationally representative data from the Family and Child Experiences Survey 2009 Cohort (n = 2,842), this study examined the implications of 3- and 4-year-old’s absences from Head Start for their early academic learning. The findings from this study revealed that children who missed more days of school, and especially those who were chronically absent, demonstrated fewer gains in areas of math and literacy during the preschool year. Moreover, excessive absenteeism was found to detract from the potential benefits of quality preschool education and was especially problematic for the early learning of children who entered the Head Start program with a less developed skill set. Implications for policy and practice are discussed. Keywords Absenteeism; Head Start; Academic achievement; Classroom quality; FACES 2009 Despite the increased interest in early childhood programs as a means of minimizing long- term academic disparities (Duncan & Magnuson, 2013), there is a widespread belief among parents that early childhood programs are not school or are less important than later schooling (Ehrilch, Gwynee, Pareja, & Allensworth, 2014). Reflecting these notions, recent estimates from urban communities reveal that absenteeism is rampant among preschoolers (Dubay & Holla, 2015; Ehrilch et al., 2014). To date, however, the focus of the school attendance literature has generally been on the K-12 educational system and, thus, we know little about the implications of preschool absences. Given the large investments being made in early childhood programs both in the United States and globally (Duncan & Magnuson, 2013), we need to consider the ramifications of absenteeism for children’s early learning, especially in programs such as Head Start, the largest federally funded preschool program in the United States. As brief background, Head Start is a government program that was established in 1965 as part of President Lyndon B. Johnson’s War on Poverty. Although Head Start began as an eight-week summer demonstration project, it has since expanded to a nine-month part- and full-day preschool program serving roughly one million 3- and 4-year- olds per year. Since its inception, Head Start was designed to “promote the school readiness of low-income children by enhancing their cognitive, social, and emotional development” *Correspondence concerning this article should be addressed to the first author at the Center for Advanced Study of Teaching and Learning, University of Virginia, PO Box 800784, Charlottesville, VA 22908-0784 (aa2zz@eservices.virginia.edu). HHS Public Access Author manuscript Child Dev. Author manuscript; available in PMC 2019 July 01. Published in final edited form as: Child Dev. 2018 July ; 89(4): 1088–1098. doi:10.1111/cdev.12800.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 73 (Head Start Act, 2007). To do so, Head Start takes a whole child model to early childhood education and provides comprehensive educational, nutritional, and social services to low- income children and their families (Zigler & Muncheow, 1992). Despite the dearth of empirical inquiry regarding preschool absences, research on elementary school absenteeism has consistently found that children who miss more days of school perform more poorly in areas of academic achievement as compared with their classmates with a better school attendance record (Chang & Romero, 2008; Gottfried, 2009, 2010, 2011; Gershenson et al., 2015; Morrissey Hutchison, & Winsler, 2014; Ready, 2010). These pervasive negative associations are multi-factorial: children who are frequently absent are (a) more often from disadvantaged households and at-risk for less optimal academic achievement (Morrissey et al., 2014; Ready, 2010) and (b) exposed to fewer days of instructional environments (Arbour et al., 2016). The elementary school literature also highlights the fact that school absences are influenced by numerous factors that cut across different layers of the family, community, and school context, and are not solely determined by child health (Gottfried, 2015). Accounting for these ecological factors may be even more necessary when examining absenteeism in preschool, as parents are less likely to view preschool attendance as critical, compared with attendance in elementary school years (Ehrilch et al., 2014). However, we know little about preschool absences. This is in part due to the fact that unlike the K-12 educational system, school attendance is not mandated by law for preschoolers and, thus, is not always tracked at the child-level by preschool programs like Head Start. Although kindergarten is also not mandated by law in most states, there are typically administrative records of kindergartner’s school absences, as it is part of the formal schooling system. Two recent studies from Baltimore (Connolly & Olson, 2012) and Chicago (Ehrilch et al., 2014) are of note, however, as they have provided some of the first empirical evidence that indicate that absenteeism is especially high (20–27% in Baltimore and 36–45% in Chicago) and particularly problematic during the preschool year. Specifically, data from these two communities indicate that a sizable number of children who were chronically absent— defined as missing 10% of the school year (Balfanz & Byrnes, 2012)—as early as preschool were documented as chronically absent throughout elementary school. These community efforts also revealed that preschool absenteeism was associated with lower academic test scores through the end of second grade, with effect sizes ranging from roughly 5–15% of a standard deviation. While these two studies have greatly contributed to the discourse surrounding absenteeism prior to the start of formal schooling, there has not been a national analysis of preschool attendance. Studying preschool attendance, especially among low-income preschoolers, is crucial as many early childhood programs operate under the compensatory hypothesis (Sameroff & Chandler, 1975), which argues that at-risk children can benefit most from their participation. Supporting these theories, a number of studies on Head Start have revealed that children who start school with a less developed skill set benefit more from preschool than children with a more developed skill set (Choi et al., 2016; Puma et al., 2010). Less often discussed is that, by missing school, these children who begin the year with the lowest skills have fewer opportunities to make ground on their more skilled peers. In this way, Ansari and Purtell Page 2 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 74 absenteeism may be particularly problematic for children who enter the program with the lowest skills (for similar analyses with elementary school absences see, Chang & Romero, 2008). Understanding the role of children’s absenteeism in early childhood programs also has important implications for policy and practice as preschool absences may be one of the key reasons why prior evaluations of Head Start (Puma et al., 2012) and meta-analyses of classroom quality (Keys et al., 2013) have yielded only small academic benefits for children. Reflecting these possibilities, a recent experimental evaluation of preschool programs in Chile (Arbour et al., 2016) found that children who were less likely to be absent from school made greater academic gains as a result of their participation in the preschool intervention as compared with children who were more likely to be absent. Such studies, however, are few and far between. Yet, such possibilities are supported by theories of social integration and intergenerational-bonding, which argue that, beyond the parent-child relationship, one of the most important relationships children develop are those with their teachers (Crosnoe, Johnson, & Elder, 2004). This relationship is a source of support that develops from the daily interactions with children, which in turn, can facilitate children’s early learning (Hatfield, Burchinal, Pianta, & Sideris, 2015). Thus, in addition to missing instructional interactions, the development of supportive relationships between teachers and children may be particularly impacted by children’s school absences because it limits how often children can interact with their teachers. Thus, the goal of this report was to address these gaps in knowledge. To this end, we address the following three research questions: (1) What are the implications of absenteeism for children’s early academic learning over the course of the Head Start year? (2) Are children with lower academic skills at the start of preschool more susceptible to the influence of absenteeism? And (3) Does absenteeism attenuate the academic benefits of quality classroom environments? We hypothesized that all children would perform more poorly over time in areas of early academics when they were more frequently absent from Head Start; however, those who entered school with the lowest skills would be more likely to be negatively affected. We also expected that school absences would minimize the potential benefits of quality preschool environments. Method The FACES 2009 cohort followed a nationally representative sample of 3,349 3- and 4-year- old first time Head Start attendees across 486 classrooms (for sampling information, see Malone et al., 2013). With a response rate of roughly 94%, children and families were followed through the end of the kindergarten year (fall 2009, spring 2010, spring 2011, and for 3-year-olds, spring 2012) across all fifty states and the District of Columbia. FACES 2009 was funded by ACF and collected by Mathematica Policy Research and their partners. The data collection includes surveys with Head Start teachers and directors, parent surveys, direct child assessments, and classroom observations (West, Tarullo, Aikens, Malone, & Carlson, 2011). For the purposes of the current investigation, we focus on the first two waves of data collection (fall 2009 and spring 2010), as these waves capture the Head Start year. We excluded 444 children who did not have a valid longitudinal weight for these two waves. Ansari and Purtell Page 3 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 75 However, all analyses include the longitudinal weight, which accounts for participants’ non- response at the second wave of data collection. We also excluded 63 children who were in a home-based program, resulting in a final analytic sample of 2,842 children. On average, our final sample of children (50% female) were 3.84 years of age (SD = 0.55; range 2.66–5.00 years of age) with the majority coming from ethnic minority households (36% Latino, 34% Black, 8% Asian or other). The remaining 21% of children were identified as White by their parents. Over half of children came from a household without two parents (53%) and with an unemployed mother (52%), and on average, mothers had a high school diploma (for other descriptives see Table 1). Measures Below, we describe our focal measures. The reliability estimates for all of the child outcomes come from the FACES 2009 User’s Guide (Malone et al., 2013). Absenteeism—During the spring of 2010 parents were asked, “Approximately how many days has [CHILD] been absent since the beginning of the school year?” Responses were continuously measured and ranged from 0 to 20. Because not all parents reported on their children’s absences at the same time point (52% in March; 28% in April; and 20% in May), and because some programs operated for four rather than five days per week, we created an indicator of the proportion of days missed as a fraction of the days children were enrolled in school. To create this measure, we first used parents date of assessment during the spring term to gauge how long children were enrolled in Head Start and divided the number of days children were absent by the number of months they were enrolled in school. This measure provided us with the number of days children were absent per month. Next, we multiplied the number of days children were absent per month by nine (i.e., the months of the school year). Finally, we divided this estimate by the number of days the program was in operation, which provided us with the proportion of the school year children were absent (see Appendix Figure 1 for a histogram of the distribution of children’s school absences). Chronic absenteeism was defined as missing 10% or more of the school year (Balfanz & Byrnes, 2012). As a precaution, we also used the raw number of days children were absent and the results were the same as those presented below. Academic achievement—Three domains of children’s academic achievement were directly assessed at the beginning (fall 2009) and end (spring 2010) of the school year. First, children’s language skills were measured using the Peabody Picture Vocabulary Test (PPVT, Dunn & Dunn, 1997; fall α = .97 and spring α = .95). To capture children’s language skills, assessors asked children to point to one of four pictures that best illustrated the meaning of a word that was said aloud by the assessor. Because the W scores, which provides information on children’s absolute performance at any given time point and captures growth in children’s learning and development, were not available for the Spanish version of the PPVT, we used the standard scores. Next, two subscales from the Woodcock-Johnson (Woodcock, McGrew, & Mather, 2001), the Letter Word Identification (fall 2009: α = .85 and spring 2010: α = . 83) and Spelling Word (fall 2009: α = .79 and spring 2010: α = .83), were administered to children to capture their literacy skills. These assessments captured children’s ability to identify and write letters. Because the pattern of findings were the same across both the Ansari and Purtell Page 4 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 76 Woodcock Johnson subscales, we created an overall composite of literacy achievement using the W scores, allowing us to assess growth over time. Finally, children’s math skills were directly assessed with the Woodcock-Johnson Applied Problems subscale (Woodcock, McGrew, & Mather, 2001; fall 2009: α = .87 and spring 2010: α = .89). We used the W score for this assessment, which captured growth in children’s ability to analyze and solve simple math problems. For all assessments, children who came from non-English speaking homes were assessed with the Simon Says and Art Show subscales of the Preschool Language Assessment Survey (preLAS; Duncan and DeAvila 1998; α = .88–.90). The Simon Says screener assessed children’s English receptive vocabulary (e.g., children were asked to touch their toes), whereas the Art Show assessed children’s English expressive language skills (e.g., children were asked to identify what was in each picture). The preLAS was used to determine whether children from non-English speaking homes had the English language skills necessary to take the assessment in English. Those who failed the test were assessed with the Spanish version of the assessments (roughly 95% of children who failed the screener at the start of the year spoke Spanish). For these children, we used their scores on the Spanish assessments, which demonstrated similar levels of internal consistency as the English measures (Malone et al., 2013). For the small number of non-Hispanic children who did not speak Spanish at home and who failed the language screener, test score data from the first wave were imputed using missing data procedures. It is important to note that (a) almost all of the non-Hispanic children who did not speak Spanish passed the language screener during the spring semester, and thus, had valid scores at the end of the year and (b) our results were not sensitive to the inclusion (or exclusion) of these children. All analyses included an indicator of children’s assessment language (84% English-English; 7% Spanish-Spanish; and 9% Spanish-English). Classroom quality—During the spring of 2010, all Head Start classrooms were observed and rated on the CLASS (Pianta, La Paro, & Hamre, 2008). The CLASS is based on a 7- point Likert scale (1–2 = low to 6–7 = high) and was used to measure instructional and socio-emotional aspects of the classroom with a focus on teacher-child interactions. Covariates—To reduce the possibility of spurious associations, both our main effect and interaction models adjusted for a full set of covariates that were derived from the fall of the Head Start year and reported on by either parents or teachers (see Table 1). It is important to note that all analyses also accounted for children’s school entry skills (i.e., lagged dependent variables), which are recognized as one of the strongest adjustments for omitted variable bias (NICHD & Duncan, 2003). In doing so, our analyses consider the extent to which absenteeism was associated with changes in children’s academic achievement across the Head Start year. Analysis plan All focal analyses were estimated within a regression framework using the Mplus program and included all covariates listed in Table 1 (Muthén & Muthén, 1998–2013). To examine the associations between absenteeism and children’s early academic learning, we estimated Ansari and Purtell Page 5 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 77 three separate models individually predicting children’s language, literacy, and math skills (Model 1). However, it might be that one additional absence may not greatly matter for children’s early learning, and instead, it is chronic levels of absences that impact children’s academic development; thus, we also estimated similar models with a dichotomous indicator of chronic absenteeism replacing the continuous absenteeism variable (Model 2). Next, to test for moderation, we interacted absenteeism with each of the moderators, one at a time (Model 3: children’s initial skill; Model 4 classroom quality). For these moderation analyses, we used the continuous version of absenteeism. If there was evidence for moderation, we plotted the interactions by calculating the predicted outcome scores for different combinations of absenteeism and the moderator using standard deviation [SD] cut points (Aiken & West, 1991). Specifically, we used + and −1 SDs for our thresholds. Standard errors were adjusted in all models by clustering at the classroom level in order to account for dependence in child outcomes and all regression models were weighted to be nationally representative. Item-level missing data was minimal (average 6%, range 0–19%) and was addressed with full information maximum likelihood estimation. Finally, all continuous variables were standardized (mean of 0 and SD of 1), and thus, our parameter estimates indicates how many SDs children’s early academic skills would change per SD increase in absenteeism. It should be noted that in discussing our results, we focus on the general pattern of findings without a p-value adjustment for multiple comparisons; however, we also present results adjusting for multiple comparisons using the Benjamini adjustment (Benjamini & Hochberg, 1995) for each predictor. We note when results were discrepant between the adjusted and non-adjusted models. In addition to the regression models discussed above, we also estimated supplementary propensity score models (Rosenbaum & Rubin, 1983), which are often used to minimize selection bias. Specifically, we estimated three sets of propensity score models, namely: (1) weighted models with the propensity score covariate (PSC); (2) unweighted models with the PSC; and (3) models with propensity score matched (PSM) samples that were weighted with the propensity score weight. We included both PSM and the PSC models because PSM is generally used for dichotomous predictors, whereas the PSC approach can be used with both dichotomous and continuous predictors (Austin, 2011). We estimated both weighted and unweighted PSC models because the unweighted model parallels our PSM model that are not nationally representative, whereas the weighted approach best parallels our weighted regression models that are nationally representative. All propensity scores were generated within an unweighted logit (chronic absenteeism) or OLS (absenteeism) framework. Additionally, all propensity score analyses included the covariates listed in Table 1 (doubly robust estimation; Funk et al., 2011). Results We begin by discussing the descriptive patterns of absenteeism and the bivariate associations between the sample characteristics and children’s school absences. For the bivariate correlations, we focus on the general patterns that emerged among our covariates and both absenteeism and chronic absenteeism. Then, we address our focal three research objectives before we close with a brief description of our propensity score models. Ansari and Purtell Page 6 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 78 Descriptives and Bivariate Correlates of School Absences As can be seen in Table 1, children in Head Start missed approximately 5.5% of the school year (SD = 4.3%, range 0.0–23.8%) and roughly 12% of the full sample of children was chronically absent. Translated into days of school missed, these estimates indicate that on average, children in Head Start were absent for roughly eight days of the year. Children who were chronically absent missed an average of 22 days. A number of our child, household, and classroom factors were also related to children’s school absences (see Table 1). Specifically, Black and Latino children were less likely to be absent and chronically absent from Head Start than White children, as were children who came from households without two parents (versus households with married parents). In contrast, children were more likely to be absent from Head Start when their mothers exhibited more depressive symptoms and were unemployed (versus employed full time). In terms of classroom characteristics, we found that children who were enrolled in larger classrooms, bilingual classrooms, and classrooms that operated for more hours per week (e.g., part- vs. full-day) were less likely to be absent from school. Children were also less likely to be absent from Head Start when their teachers received a higher hourly wage. For other associations that emerged for only one of our two absenteeism measures, see Table 1. Absenteeism and Children’s Early Learning Although absenteeism was not associated with children’s language development in our multivariate models (see Table 2), children who were more frequently absent demonstrated smaller gains in literacy and math with effect sizes corresponding with 5–6% of a SD (see Appendix Table 1 for the associations between the covariates and outcomes). Findings for chronic absenteeism were stronger, with children who were chronically absent exhibiting even smaller gains, with effect sizes of 13–14% of a SD. In practical terms, the effect sizes for chronic absenteeism translate to roughly two (math) to three (literacy) months of lost academic skill gains (calculated by dividing the standardized difference in test scores by the regression slope for children’s age; Bradbury et al., 2011). Moderators of Absenteeism and Children’s Early Learning As can be seen in Table 2, results from our multivariate analyses also suggested that children’s school entry skills were fairly stable, and although missing school was generally associated with less optimal academic achievement for all children, it was most problematic if children started school with the lowest language and literacy (but not math) skills. For example, the negative associations between absenteeism and children’s literacy development were 16% of a SD greater for children who entered the Head Start program with low as opposed to high literacy skills. Finally, although all children exhibited greater gains in areas of early literacy when they experienced higher quality interactions with their teachers, these associations were larger when children were less frequently absent (see Figure 1; this estimate was only marginally significant with a Benjamini adjustment). While not reaching conventional standards of significance in either our adjusted or non-adjusted models (p = .06), similar—albeit slightly smaller—patterns emerged for children’s language development. Thus, these results indicate Ansari and Purtell Page 7 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 79 that high quality programs were associated with improvements in children’s early language and literacy skills, but children who were more frequently absent did not reap the maximum benefit. Propensity Score Models After balancing the comparison conditions (results are available upon request), we found that our propensity score models confirmed the conclusions discussed above (see Appendix Table 2). As before, children who were more frequently absent exhibited fewer gains in math and literacy (but not language skills) over the course of the Head Start year, and these associations were stronger at chronic levels of school absences. Discussion Despite the growing investments in early childhood programs (Duncan & Magnuson, 2013), children’s school attendance has been largely ignored and remains unmeasured in most early care and education programs (Mendez, Crosby, & Helms, 2016). Thus, the purpose of this research brief was to provide a preliminary exploration of the implications of absenteeism for children’s early academic achievement using a nationally representative Head Start sample. Drawing on data from the FACES 2009 cohort, this study sought to illustrate the importance of measuring absenteeism in preschool while also encouraging new work in an area that has remained relatively underdeveloped. The results of our study have four take home messages. First, our descriptive results suggest that children missed roughly eight days of the school year and 12% of children were chronically absent and, therefore, missed an average of 22 days. We also documented a few potential determinants of school absences; for example, minority children were less likely to be absent as compared with White children, as were children who were enrolled in school for longer hours and in larger and bilingual classrooms. When taken together, these descriptive results indicate that absenteeism is a prevalent in Head Start, but that certain groups of children are at greater risk of missing time than others. Future programs designed to increase attendance may be strengthened by tailoring their efforts to these subgroups. Second, results from our focal multivariate models confirmed some of what is known about the relations between absenteeism and achievement during the primary school years and beyond and suggest that these patterns hold true in preschool (Gottfried, 2009, 2011; Gershenson et al., 2015; Morrissey et al., 2014; Ready, 2010). Children who missed more days of preschool demonstrated fewer gains in literacy and mathematics, and the detrimental effects were greater among children who were absent for more than 10% of school year (chronic absenteeism; Chang & Romero, 2008; Ready, 2010). Put in context with other influences on children’s early learning, the associations between chronic absenteeism and children’s early learning were roughly three to four times as large as that of meta-analyses of classroom quality (meta-analytic E.S. of classroom quality [Keys et al., 2013]: 0.03–0.05 SDs versus chronic absenteeism E.S.: 0.13–0.14 SDs). Within the context of our study, the effects of chronic absenteeism were 1.4 (literacy: E.S.quality = .10 versus E.S.chronic absenteeism = 0.14) to 6.5 (math: E.S.quality = 0.02 versus E.S.chronic absenteeism = Ansari and Purtell Page 8 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 80 0.13) times larger than the effects of classroom quality, and amounted to 2–3 months of math and literacy development. Despite the links between absenteeism and children’s early literacy and math development, preschool absences were not associated with children’s language development. This finding parallels prior work, which shows that preschool programs often do not influence children’s language skills but do influence their early literacy (National Early Literacy Panel, 2008). Thus, these findings suggest that future evaluations of Head Start should consider a treatment-on-the-treated model that incorporates children’s absenteeism to estimate the full potential of the program. Third, consistent with the compensatory model of education (Sameroff & Chandler, 1975) and Ready’s study of absenteeism in kindergarten (2010), preschool absences were most detrimental for children who entered the program with the lowest language and literacy skills. Although we did not see direct associations between absenteeism and language development, it may be that absences are more predictive for children who enter the classroom with low language skills, as they may be more dependent for classroom exposure to new language skills than their peers who enter school with higher language skills. Finally, consistent with the existing literature (Keys et al., 2013), results from this study revealed that the quality of teacher-child interactions facilitated children’s literacy development. For the first time, however, our results show that these benefits were considerably larger for children who were infrequently absent. The implications of this finding are quite important in light of the fact that the benefits of quality preschool education have proven to be smaller than expected (Keys et al., 2013). The results of this study suggest that these patterns may partly be due to the attenuating effect of chronically absent children and, thus, should be considered in future studies of classroom quality. Even though this study is the first national analysis of preschool absences, these general points of discussion need to be interpreted in light of a few limitations. The primarily limitation of our work is that of measurement. Unlike some studies on primary school absences that have been able to draw on data from school attendance records (Gershenson et al., 2015; Morrissey et al., 2014), we were restricted to parent reports of children’s school attendance. Considering that attendance is rarely tracked at the preschool level, there are not many other options to estimate these associations. In fact, the FACES dataset is one of two national early childhood datasets that has any information on children’s preschool absences (Mendez et al., 2016). Even so, it is important to acknowledge that our estimates of preschool absences were lower than those of Ehrlich and colleagues (2013) and Connolly and colleagues (2012), which may reflect social desirability. That is, there may be some bias due to parents’ underreporting of children’s preschool absences, which increase the risk of null findings. Despite this potential source of bias, the effect sizes reported in our study and the effect sizes reported in Chicago and Baltimore are of the same magnitude, suggesting that this bias is unlikely to affect the associations between the focal variables of interest. Moreover, our estimates of preschool absences are comparable to national estimates of kindergarten absences (Gershenson et al., 2015; Gottfried, 2015), which is of note because kindergarten, like preschool, is not mandatory in most states. Next, we used a sample of Head Start children, which is an important strength as Head Start is the largest federally funded preschool program serving low-income children; however, this line of work should be extended to other types of preschool programs before we can generalize these patterns to Ansari and Purtell Page 9 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 81 other populations. Finally, although we (a) controlled for a rich set of covariates and (b) we estimated propensity score models, these analyses do not imply cause and effect. One purpose of a brief report is to provide new findings that extend the existing literature and spur future research. Our results inform the discourse surrounding absenteeism in preschool by illustrating the scope and consequences of school absences in Head Start for children living in poverty. When taken together, these results indicate that future researchers need to pay closer attention to the role of preschool absences in developmental and educational research. These findings also have practical implications and suggest that preschool teachers and administrators may want to exert some effort to reduce school absences. One important route may be to discuss challenges to attendance that parents are facing and work with them to reduce these barriers. Work with older children has shown that absenteeism is rarely due to one factor; thus, working with families to reduce multiple causes may be necessary (Teasley, 2004). One successful elementary model assigned monitors to engage with both families and school staff to increase attendance; this type of model may be particularly useful in Head Start, which already strives to increase parent- center communication (Lehr, Sinclair, & Christenson, 2004). The early childhood field should also consider ways to make sure that parents understand that preschool is an educational program, not just a daycare, and that school absences are problematic as recent research on prekindergarten has shown that these types of parental beliefs are critical to increasing school attendance (Katz, Johnson, & Adams, 2016). Setting and communicating clear attendance expectations and engaging parents in school are emerging as key ways to change parental beliefs and children’s preschool absences (Katz et al., 2016). Ultimately, if our results are confirmed with other samples of children and families from across the country then we can draw more definitive conclusions regarding preschool absences as a source of inequality. In the meantime, however, an important first step is to ensure that future data collection efforts gather information on children’s preschool absences. By using a large and nationally representative dataset of Head Start children, this study pushes this agenda forward by providing some of the first insight into the harmful implications of absenteeism for low-income children’s academic learning during the preschool year. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Acknowledgments The authors acknowledge the support of grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD069564, PI: Elizabeth Gershoff; R24 HD42849, PI: Mark Hayward; T32 HD007081-35, PI: Kelly Raley) and the Institute of Education Sciences, U.S. Department of Education (R305B130013, University of Virginia). References Administration for Children and FamiliesHead Start Performance Standards Washington, DC: U.S. Department of Health and Human Services; 2015 45 CFR Ch. XIII (10-1-09 Edition) [With Part 1306.32, Center-based Program Option] Ansari and Purtell Page 10 Child Dev. 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Chicago: University of Chicago Press; 1975 187244 Teasley ML. Absenteeism and truancy: Risk, protection, and best practice implications for school social workers. Children & Schools. 2004; 38:117–128. DOI: 10.1093/cs/26.2.117 West J, , Tarullo L, , Aikens N, , Malone L, , Carlson BL. OPRE Report Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services; 2011 FACES 2009 Study Design; 20119 Woodcock RW, , McGrew KS, , Mather N. Woodcock-Johnson III tests of achievement Itasca, IL: Riverside Publishing; 2001 Zigler E, , Muenchow S. Head Start: The Inside Story of America’s Most Successful Educational Experiment New York, NY: Basic Books; 1992 Ansari and Purtell Page 12 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 84 Figure 1. An illustration of the conditional effects of quality teacher-child interactions on children’s literacy skill gains, as a function of their absences from school. Notes. Low absences correspond to roughly two days (1.18% of the school year; roughly 11% of the sample were at or above this threshold), whereas high absences correspond to approximately 15 days (9.78% of the school year; roughly 13% of the sample were at or above this threshold). Low quality corresponds to a score of 3.58 on the CLASS (roughly 15% of the sample were at or above this threshold) and high quality corresponds with 4.56 on the CLASS (roughly 18% of the sample were at or above this threshold). Ansari and Purtell Page 13 Child Dev. Author manuscript; available in PMC 2019 July 01.Author Manuscript Author Manuscript Author Manuscript Author Manuscript 85 Author Manuscript Author Manuscript Author Manuscript Author Manuscript Ansari and Purtell Page 14 Table 1 Descriptive statistics for study variables. Mean (SD) or proportion Bivariate correlations Absenteeism Chronic absenteeism Child/household characteristics Percent of days absent 5.48 (4.30) — — Chronically absent 0.12 — — Child is male 0.50 0.04 0.00 Child race White 0.21 — — Black 0.34 −0.26 ***−0.16 *** Latino 0.36 −0.15 ***−0.11 *** Asian/other 0.08 −0.09 *−0.04 Child age (months)46.09 (6.65)−0.04 −0.05 * Child health a 4.29 (0.90)−0.04 *−0.03 Four year old cohort 0.43 −0.01 −0.03 Months between fall and spring child assessments 5.85 (1.47) 0.02 0.02 Language of assessment (fall-spring) English-English 0.84 — — Spanish-Spanish 0.07 0.01 0.01 Spanish-English 0.09 −0.03 −0.02 Mothers’ marital status Married 0.29 — — Single 0.18 −0.05 −0.02 Not two parent household 0.53 −0.09 ***−0.05 * Mothers’ years of education 12.00 (1.84) 0.01 0.01 Mothers’ age 28.83 (5.89)−0.04 −0.04 Household size (children and adults) 4.61 (1.61)−0.06 **−0.05 * Mothers’ employment Full time 0.27 — — Part time 0.21 0.06 0.05 Unemployed 0.52 0.08 *** 0.08 *** Mothers’ depressive symptoms b 4.89 (5.82) 0.09 *** 0.07 *** Ratio of income to poverty c 2.52 (1.36) 0.04 0.01 Number of moves in the last 12 months 0.49 (0.83) 0.03 0.02 Cognitive stimulation d 0.79 (0.16) 0.02 0.01 Frequency parent spanked child 0.67 (1.26) 0.04 * 0.01 English household language 0.76 −0.02 −0.01 Classroom characteristics Child/teacher ratio 8.55 (2.25)−0.05 **−0.04 * Child Dev. Author manuscript; available in PMC 2019 July 01. 86 Author Manuscript Author Manuscript Author Manuscript Author Manuscript Ansari and Purtell Page 15 Mean (SD) or proportion Bivariate correlations Absenteeism Chronic absenteeism Child/adult ratio 7.36 (2.13)−0.06 **−0.06 ** Class size 17.28 (2.17)−0.07 **−0.07 *** Hours of school per week 26.36 (11.49)−0.10 ***−0.12 *** Program meets five days a week 0.75 −0.10 ***−0.10 *** Full day program 0.60 −0.11 ***−0.11 *** Other languages used in the classroom (yes) 0.34 −0.04 *−0.04 * Quality of teacher-child interactions (CLASS) 4.07 (0.49)−0.02 −0.03 Global classroom quality (ECERS-R) 4.27 (0.77) 0.01 0.00 Teacher characteristics Depressive symptoms b 4.25 (4.60) 0.01 0.00 Years teaching 12.71 (8.68)−0.03 −0.01 Years of education 14.99 (1.79)−0.06 **−0.01 Degree in early childhood education 0.92 −0.01 −0.00 Hourly salary 14.11 (4.79)−0.11 ***−0.06 ** Number of benefits e 6.55 (2.39) 0.06 * 0.05 Children’s outcomes Language (fall) 81.62 (19.76) 0.07 *** 0.05 * Language (spring) 86.12 (16.85) 0.06 ** 0.04 Letter-word identification (fall)304.98 (24.37)−0.02 0.01 Letter-word identification (spring)322.75 (27.86)−0.08 ***−0.05 * Spelling (fall)344.02 (29.31)−0.03 −0.02 Spelling (spring)363.34 (30.32)−0.08 ***−0.09 *** Math (fall)373.42 (25.70)−0.03 −0.03 Math (spring)386.93 (24.77)−0.06 **−0.05 * Notes. aChildren’s health was reported by parents using a 5-point Likert scale (1= Poor, 5= Excellent). bBoth parents’ and teachers’ depressive symptoms were measured via 12 questions from the short form of the Center for Epidemiological Studies Depression Scale (α =.91; Radloff, 1977) with scores ranging from 0–36. cThe ratio of income to poverty measure was quasi-continuous with scores ranging from 1 (< 50% of the federal poverty line [FPL]) to 6 (>200% of the FPL). dThe cognitive stimulation measure was a composite of 12 items that captured parents’ household investments during the past week (e.g., told child a story; taught child letters or numbers). eTeachers benefits (e.g., paid vacation, sick leave) was based on a 0–9 scale. ***p < .001. **p < .01. *p < .05. Child Dev. Author manuscript; available in PMC 2019 July 01. 87 Author Manuscript Author Manuscript Author Manuscript Author Manuscript Ansari and Purtell Page 16 Table 2 Multivariate results of children’s academic achievement as a function of absenteeism. Child Outcomes a Language Literacy Math Main Effects of Absenteeism Absenteeism (Models 1, 3 and 4)−0.00 (0.02)−0.05 ** (0.02) −0.06 ** (0.02) Chronic Absenteeism (Model 2)−0.03 (0.05)−0.14 ** (0.05) −0.13 * (0.06) Main Effects of Moderators School Entry Skills (Models 1–4)0.62 *** (0.02) 0.60 *** (0.02) 0.54 *** (0.02) Quality of Teacher-Child Interactions (Models 1–4)0.01 (0.02)0.10 *** (0.02) −0.02 (0.03) Interaction Terms b Absenteeism X School Entry Skills (Model 3)0.06 * (0.03) 0.04 ** (0.01) 0.04 (0.03) Absenteeism X Quality of Teacher-Child Interactions (Model 4)−0.03 † (0.02) −0.03 * (0.01) −0.00 (0.02) Notes. Bolded coefficients were statistically significant at p < .05 and italicized coefficients were significant at p < .10 with a Benjamini false discovery adjustment. All continuous variables were standardized within wave (mean of 0 and standard deviation of 1), and therefore the unstandardized regression coefficients in this table correspond to effect sizes. aAll models controlled for the covariates listed in Table 1 and were clustered at the classroom level. bBecause the variables above have a mean of 0, the main effect coefficients were the same across both the interaction and main effect models. Separate models were estimated for each individual interaction. ***p < .001. **p < .01. *p < .05. †p < .10. Child Dev. Author manuscript; available in PMC 2019 July 01. 88 Early Childhood Research Quarterly 32 (2015) 160–173 Contents lists available at ScienceDirect Early Childhood Research Quarterly Can center-based childcare reduce the odds of early chronic absenteeism? Michael A. Gottfried ∗ Gevirtz Graduate School of Education, UC Santa Barbara, United States a r t i c l e i n f o Article history: Received 6 August 2014 Received in revised form 26 March 2015 Accepted 2 April 2015 Available online 16 April 2015 Keywords: Center-based care Prekindergarten Kindergarten Chronic absenteeism a b s t r a c t This study was the first to position itself in the intersection on research on center-based care and on chronic absenteeism. Given the growth in the utilization of center-based care and given the recent vocal- ized policy concerns of the detrimental effects of chronic absenteeism in early school years, this study inquired as to whether attending center-based care predicted differential odds of early absence patterns. Using a newly-released national large-scale study of children (the Early Childhood Longitudinal Study – Kindergarten Class of 2010–2011), the findings indicated that children who attended center-based care in prekindergarten had lower odds of being chronically absent in kindergarten. The conclusions were consistent even after employing multiple methodological approaches (fixed effects, propensity score matching) as well as exploring multiple definitions of chronic absenteeism, though were not differenti- ated by socioeconomic status. Additional noteworthy findings are discussed, including the significance of children’s internalizing symptoms and parental mental health. © 2015 Elsevier Inc. All rights reserved. Introduction When considering the short-term effects of attending center- based childcare, research has predominantly focused on achieve- ment and socioemotional outcomes (Claessens, 2012; Crosnoe, 2007; Loeb, Bridges, Bassok, Fuller, & Rumberger, 2007; Magnuson, Rhum, & Waldfogel, 2007; Turney & Kao, 2009). Research gener- ally supports that attending center-based care boosts achievement (Burger, 2010; Loeb et al., 2007; Loeb, Fuller, Kagan, & Carrol, 2004; National Institute of Child Health and Human Development [NICHD], 2006). Research mostly links attending center-based care to null or lower socioemotional development and null or higher behavioral issues (Baker, Gruber, & Milligan, 2008; Belsky et al., 2007; Herbst & Tekin, 2010; Loeb et al., 2007; Magnuson et al., 2007; NICHD, 2006; Yamauchi & Leigh, 2011). Early academic and socioemotional outcomes are certainly critical to examine, particularly as they signal school readiness. However, in the discourse surrounding the influence of attending center-based childcare, research has not considered how going to center-based care may be linked to early patterns of chronic absen- teeism. Although no absolute definition exists, chronic absenteeism is defined here as missing at minimum two or more weeks of school ∗Tel.: +1 8058935789. E-mail address: mgottfried@education.ucsb.edu for any reason in a given year (Balfanz & Byrnes, 2012; Gottfried, 2014). This gap in examining school absences as outcomes is criti- cal to address: The short- and long-term negative consequences associated with excessive school absences cannot be overstated, including lower achievement, increased behavioral issues, lower social development, greater chances of grade retention, higher odds of school dropout, increased risk of the use of drugs and alcohol in young adulthood and adulthood, and lower employment prospects (Alexander, Entwisle, & Horsey 1997; Broadhurst, Patron, & May- Chahal, 2005; Chen & Stevenson, 1995; Connell, Spencer, & Aber, 1994; Ekstrom, Goertz, Pollack, & Rock, 1986; Finn, 1993; Gottfried, 2009, 2010, 2014; Hallfors et al., 2002; Kane, 2006; Morrissey, Hutchison, & Winsler, 2014; Newmann, 1981). It is estimated that somewhere between 10% and 15% of young school-aged children are chronically absent and thus susceptible to these negative con- sequences (Balfanz & Byrnes, 2012; Romero & Lee, 2007). This estimate is larger for students of lower socioeconomic status (SES) (Ready, 2010), thereby exacerbating these risks. In elementary school, chronic absenteeism is highest in kinder- garten (Balfanz & Byrnes, 2012; Romero & Lee, 2007). The notion of ‘chronic’ absenteeism is fairly nascent in both policy and research, and therefore most research in early school absences have not considered the effects of chronic absenteeism per se (as opposed to greater/fewer school absences) (Gottfried, 2014). The few research studies in the area of early chronic absenteeism found negative effects. Chang and Romero (2008) linked chronic http://dx.doi.org/10.1016/j.ecresq.2015.04.002 0885-2006/© 2015 Elsevier Inc. All rights reserved. 89 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 161 absenteeism in kindergarten to lower first grade academic per- formance. Connolly and Olson (2012) linked chronic absenteeism in kindergarten to lower achievement, grade retention, and future chronic absenteeism. Gottfried (2014) linked chronic absenteeism in kindergarten to lower academic and socioemotional develop- ment. Given that negative consequences of chronic absenteeism emerge in kindergarten, research has attempted to identify the drivers of school absences. Most research has focused on individual- and family-level factors. At the individual level, sig- nificant factors include educational disengagement or alienation from school (Harte, 1994; Reid, 1983). Family factors include family structure, father’s occupation, mother’s work status, house- hold size, parental involvement, mother’s age, mother’s depression and socioeconomic status (SES) (Catsambis & Beveridge, 2001; Claessens, Engel, & Curran, in press; Fan & Chen, 2001; Jeynes, 2003; McNeal, 1999; Muller, 1993; Ready, 2010; Reid, 1983;Romero & Lee, 2007; Sampson & Laub, 1994). Little work has been conducted outside of identifying individual and family factors. A significant lapse in the research on both the effects on center-based care and the drivers of chronic absenteeism is the intersection between the two. On the one side, the research on the effects of center-based care has generally remained limited to achievement and socioemotional development. Other critical early indictors of early school success or risk of failure, such as absen- teeism, have largely been ignored. On the other side, research into the drivers of chronic absenteeism have generally been limited to studying individual and family factors. In fact, altogether little is known about what programs and practices in early childhood might influence early chronic absenteeism. Additional research on the drivers of absenteeism beyond these factors will develop a more robust agenda around how to reduce this negative behavior at the onset of school entry, when the frequency of this behavior is high- est. Center-based care and early chronic absenteeism Aside from one descriptive study linking attending prekinder- garten care to lower rates of chronic absenteeism in kindergarten (Connolly & Olson, 2012), no large-scale study exists in the over- lap of childcare and chronic absenteeism. Given the positive link between center-based care and early achievement, it is reason- able to expect that center-based care is linked to lower chronic absenteeism. There are four potential ways by which center-based care might be linked to lower chronic absenteeism in kindergarten: child transitions, family logistics, health, and timing. Child transitions Childhood is filled with ecological transitions that require adap- tation to new environments (Bronfenbrenner, 1979), and school entry represents a significant ecological transition in early child- hood (Ladd & Price, 1987). Kindergarten entry requires children to face many new demands including academic challenges, adap- tation to institutional expectations, and socialization (Bensen, Haycraft, Steyaert, & Weigel, 1979; Bogart, Jones, & Jason, 1980; Holland, Kaplan, & Davis, 1974). Unsuccessful transition into kindergarten correlates with children feeling less secure about their environments and increased stress, thereby leading to school avoidance and negative feelings about school (Ladd & Price, 1987). These negative feelings materialize as absences (Ekstrom et al., 1986; Newmann, 1981) through refusal to attend school or pre- tending to be sick (Giallo, Treyvaud, Matthews, & Kienhuis, 2010). Children who attend formal preschool often have better mastery of this transition into kindergarten (Ladd & Price, 1987). No single explanation exists. However, one reason may be that center-based care provides a structured learning environment that mirrors what school will be like. Children are formally assigned to a classroom, taught by a specific set of teachers, and have regulated sched- ules with established times for instructional activities. In contrast, children who are cared for in informal settings may not gain the same experience of participating in a formal school-like schedule (Claessens, 2012). Second, children in center-based care get an early start on adapting to long periods of parental separation (Ladd & Price, 1987). Third, center-based instructors are often more aca- demically qualified than guardians in informal care alternatives such as relatives in home-like settings (Barnett, Carolan, Fitzgerald, & Squires, 2011). Therefore, in formal care, children have greater exposure to adults who more closely mirror school teachers in classrooms. Fourth, children in center-based care are often in envi- ronments with many peers, and this provides them with early opportunities to socialize, understand individual differences, and adapt to group behavior. Finally, attending center-based care pro- vides children with an early opportunity to adapt to a routine of regularly leaving the home (Ladd & Price, 1987). It is thus theorized that going to center-based care in prekinder- garten facilitates the transition into kindergarten, either by providing children with an early school-like routine or with addi- tional opportunities to adapt to interacting with adults who are similar in characteristics to schoolteachers and to interacting with other children in a classroom setting. Hence, when entering kindergarten, they have fewer adjustment demands and are more equipped to cope with new environments. This may actualize as having positive feelings about school and less anxiety about attend- ing school; feelings such as these are linked to lower odds of being absent. This framing of transitions fits into the larger literature on preparatory socialization. As described by Germain and Bloom (1999), preparatory socialization exists when spending time in one setting allows the individual to learn the processes and roles required in a future setting. Early in education, this entails learn- ing how school demands differs from those at home, which, as described above, might facilitate children learning how to develop a school-going routine or how to interact with teachers and peers in a classroom-like setting. In young adulthood, this may surface as preparing for the requirements of the working world (Golde, 1998). The concept of transitions from setting-to-setting certainly has implications beyond this study to the extent which experi- ences in one environment leads to successful functioning in a future environment. Family logistics Going to center-based care may also influence parents’ behav- ior as it relates to chronic absenteeism in kindergarten. First, a direct-effects hypothesis suggests that parents are also adjusting to the routine of sending their children to a formal non-home set- ting. Thus, center-based care may be putting both children and their families in the mindset of regularly attending school, even before starting formal schooling (Ehrlich et al., 2014). Through the actions of sending their children to center-based care, parents have an extra year to adapt to school-going logistics, such as deter- mining transportation options, shifting work schedules, instituting early-morning wake up, preparing/packing children’s breakfasts and lunches, buying appropriate school attire – all of which are sig- nificant factors of good attendance once in school (Chang & Romero, 2008). Moreover, this extra year of school-going practice may be particularly crucial for working parents, who may not have the capacity to accommodate absenteeism. Second, there may be an indirect mechanism. Once kindergarten begins, parents may hold more positive feelings and attitudes about their child’s transition to kindergarten due to the previous period of adjustment to a school-like setting via center-based care (Margetts, 2000). Hence, not only is it possible that attending center-based 90 162 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 care increases the efficiency by which parents determine the logis- tics of sending their children to formal schooling, but this efficiency also reduces parents’ stress and anxiety and increases parents’ pos- itive feelings about the transition to kindergarten, which in turn, increases their children’s positive feelings about the transition to kindergarten (Giallo et al., 2010). Positive feelings about school- going by parents or children, as mentioned earlier, are inversely related to school absenteeism. Health There are also potential unique health benefits of attending center-based care. First, programs such as Head Start are designed to increase children’s health through immunizations and health screenings (U.S. Department of Health and Human Services, 2010; Yoshikawa et al., 2013). An increase in child health established prior to kindergarten might reduce the odds of absenteeism once in kindergarten, as increased absenteeism has been shown to be highly correlated with impaired health (Ready, 2010). Second, because children in center-based care are surrounded by many children simultaneously, it is possible that children are exposed to illnesses, like chicken pox, and develop immunities before start- ing school (Ehrlich et al., 2014). This can work out in the favor of reducing absenteeism once in kindergarten. Timing The first three conceptualizations of the role of center-based care on reducing chronic absenteeism focus on the role of center- based care in the year prior to kindergarten. Therefore, the first research question is put forth as follows: RQ1: Does attending center-based care in prekindergarten reduce the odds of chronic absenteeism in kindergarten? However, attending center-based care before/after school dur- ing the kindergarten school year might also reduce chronic absenteeism. Children can also adapt to the demands of schooling once they are participating in school – they do not learn to transition solely based on the skills acquired prior to entry (Bodrova & Leong, 2005). Given this, the overall experience of participating in kinder- garten combined with center-based care before/after kindergarten hours may help to solidify transitions skills. For instance, being in center-based care before/after kindergarten hours year may rein- force the routine of attending a structured school-like environment all day. This may continue to facilitate children’s preparation and practice of going to school, hence building positive attitudes toward school and hence lowering odds of chronic absenteeism. Or, the routine of going to center-based care before/after kindergarten hours may reinforce parental routines for their child’s school atten- dance. If parents are responsible for making arrangements for both going to kindergarten and center-based care before/after the school day, then center-based care during this year may be reinforcing the logistics of attending school, which may be especially critical for working parents. Also, center-based care before/after kinder- garten hours might provide other opportunities for children to interact with teachers and children, hence providing additional reinforcement to make sense of and adapt to the ecology of school- like settings (Bodrova & Leong, 2005), thereby increasing positive school attitudes and reducing negative feelings – all of which are linked to lower chronic absenteeism (Ekstrom et al., 1986; Gottfried, 2009; Newmann, 1981). A second research question is put forth as follows: RQ2. Does the timing of attending center-based care (prekinder- garten, before/after school during kindergarten, or both) reduce the odds of chronic absenteeism in kindergarten? Family moderating factors Prior research has found that students with greater absences in kindergarten are from lower-SES families (Nauer, Mader, Robinson, & Jacobs, 2014; Ready, 2010). For instance, Applied Survey Research (2011) found that no other child characteristic provided statistically-significant differences in absence patterns besides SES. Chang and Romero (2008) found that once family SES was taken into account, racial differences were no longer signifi- cant. It appears, then, that one major determinant of early chronic absenteeism is low SES. Children from low-SES families have been shown to benefit from center-based care, academically and developmentally (Barnett, 1995; Burchinal, Campbell, Bryant, Wasik, & Ramey, 1997; Loeb et al., 2004). A key empirical issue, however, is that children from low-SES families are much less likely to utilize childcare (Meyers & Jordan, 2006). In addition, families selecting not to utilize childcare are characterized by lower maternal education, single- parent households, non-English home language, higher mobility, and maternal depression (Bainbridge, Meyers, Tanaka, & Waldfogel, 2005; Crosnoe, 2007; Fuller, Eggers-Piérola, Holloway, Liang, & Rambaud, 1996; Greenberg, 2011; Hebra et al., 2013; Hirshberg, Huang, & Fuller, 2005; Wolfe & Scrivner, 2004). In fact, Johnson, Martin, and Brooks-Gunn (2011) found that low-SES families with childcare subsidies were relatively more advantaged (on the char- acteristics mentioned above) than non-recipient eligible low-SES families. Therefore, one concern is that family selection bias drove the observed positive associations between center-based care and child outcomes: Families with the lowest levels of resources, moti- vation, or knowledge to send their children to center-based care are also the least likely to be making educational and developmental advances. As an example, children with less depressed mothers are more likely to attend center-based childcare (Hebra et al., 2013). Therefore, the association between childcare and child outcomes for low-SES children may not be solely attributed to the childcare itself. These similar family selection issues might also be obscuring the link between center-based care and early school absenteeism. For instance, families who are sufficiently organized to enroll their child in prekindergarten as well as to develop the logistics to ensure their children attend on a regular basis (and are hence less likely to drop out of programs such as Head Start) are the same fam- ilies who are more likely to ensure that their children perform better once in kindergarten, including having stronger school atten- dance. While maintaining school-going routines might be the most valuable especially for low-SES families particularly given high rates of mobility and maternal depression, it might be these fam- ilies who are least likely to be exposed to these logistics-building opportunities. Hence, the relationship between center-based care and attendance might be capturing a specific sample of families who have selected into center-based care as well as who have ensured their children remain in the program. Accounting for fam- ily selection issues, as they pertain to SES and other high-risk family attributes, is critical in this evaluation and is addressed below. As critical is addressing whether there are differences in any observed relationship between center-based care and absenteeism, but doing so by SES. Even after addressing selection, there are still several reasons to hypothesize that the above 4-pronged conceptualization (tran- sitions, logistics, health, timing) would be especially important for children in low-SES families. First, research suggests that low-SES parents do not have the resources readily available to address the going-to-school logistics required for kindergarten, such as having access to reliable transportation (Chang & Romero, 2008). Hence, center-based care during prekindergarten might induce these par- ents to address these demands and adjust to school-going routines. Second, given that children in low-SES households face greater 91 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 163 health issues (Hughes & Ng, 2003; Romero & Lee, 2007), center- based care attendance in prekindergarten may provide health benefits, such as immunization, that would in turn reduce absences once in school. Finally, low-SES parents may not have developed the skills or knowledge to help support their children in formal schooling (Chang & Romero, 2008). Additionally, parents in low-SES families might have had negative schooling experiences (Chang & Romero, 2008). Therefore, center-based care might be especially critical to develop and reinforce positive attitudes about formal schooling (and hence reducing chronic absenteeism), as they might not be exposed to this type of environment at home (Ready, 2010). Given these potential benefits, the final research question asks: RQ3. Do the relationships differ by socioeconomic status? Kindergarten is an extremely critical period that sets the foun- dation for future success (Duncan et al., 2007; Olson, Sameroff, Kerr, Lopez, & Wellman, 2005; Posner & Rothbart, 2000). There- fore, knowing what early childhood experiences reduce chronic absenteeism would help to set children on a strong trajectory. Given the lack of knowledge of the role of center-based care on chronic absenteeism and of early programs and policies that reduce this negative behavior, this study contributed to the research through these unexplored research questions. The utilization of center-based care is increasing in the U.S. (Blau & Currie, 2004; Claessens, 2012; Smith, Kleiner, Parsad, Farris, & Green, 2003; Yamauchi & Leigh, 2011). Therefore, this study addresses how to improve outcomes for an increasing number of children taking part in these early childhood programs. By inform- ing stakeholders invested in the efficacy of center-based programs alongside stakeholders invested in the reduction of chronic absen- teeism, the unification of both areas will help formulate new policy discussions around understudied but potentially influential factors of early childhood success. Method Participants Data in this study were sourced from the Early Childhood Longitudinal Study – Kindergarten Class of 2011 (ECLS-K:2011), which represents the most contemporary national-level data avail- able to evaluate the research questions in this study. This dataset was developed by the National Center for Education Statistics (NCES). The collection process included a large-scale survey design and assessment data collection of children and their families and schools. Children were in kindergarten in 2010–2011, the first year of data collection. The ECLS-K:2011 used a three-stage stratified sampling strategy, in which geographic region represented the first sampling unit, public and private school represented the second sampling unit, and students stratified by race/ethnicity represented the third sampling unit. Hence, observations in the dataset are from a diversity of school types, socioeconomic levels, racial, and ethnic backgrounds. At the time of this study, the fall and spring survey waves from 2010 to 2011 were available. To account for the loss of information, chained multiple impu- tation was employed (Royston, 2004). Consistent with Claessens et al. (in press), missing values were imputed back to the sample for which there were nonzero weights. Ten datasets were imputed, in which measures were replaced with a random sample of plau- sible values (Schafer, 1997). These ten sets of plausible values were imputed to resemble the distributions of the observed vari- ables. Outcome model results were aggregated across the imputed datasets (Schafer & Graham, 2002). Sample weights provided by NCES for the ECLS-K dataset were employed in both the imputa- tion and in the analysis. After imputation, this sample consisted of approximately n = 14,060 child observations. Sample sizes are rounded to the nearest tens digit, per NCES rules. Outcomes Table 1 presents all measures utilized in this study broken out by the four different care scenarios. The key measure was binary, indi- cating if a student was chronically absent in kindergarten. Absence information was only available from child’s teacher survey: in the spring survey wave, a child’s teacher was asked to report the num- ber of absences that a child had in that year. Each teacher selected from a discrete set of choices: 0, 1–4, 5–7, 8–10, 11–19, and 20 or more. Some consider chronic absenteeism as beginning after miss- ing a cumulative two weeks of school (Gottfried, 2014) while others indicate that chronic absenteeism occurs after missing more than 18 days of school (Balfanz & Byrnes, 2012). To be the most inclu- sive of these definitions, the primary chronic absenteeism measure equaled 1 if a student had missed more than two weeks of school (i.e., 11 or more days) and 0 otherwise. Because no one definition of chronic absenteeism exists, two subsequent outcomes were explored, as derived from a taxonomy of absenteeism in Gottfried (2014). First, “moderate” chronic absen- teeism was 11–19 absences, and 20 or more absences was classified as “strong”. Approximately 12% of the sample was chronicly absent (broken into 9% as moderate and 3% as strong). This overall per- centage conformed to prior national estimates (Balfanz & Byrnes, 2012). Center-based care Based on the fall parent survey, a child attended center-based care if his or her parents indicated that he or she went to center-based care during the prekindergarten and/or kindergarten years (asked as separate questions in the survey). Prekindergarten center-based care also included Head Start as consistent with prior research using ECLS-K data (Crosnoe, 2007). As for center-based care before/after school during the kindergarten year, note that the questions were phrased to address care that a child was receiving in addition to attending kindergarten. Therefore, even if children were in center-based private kindergarten, the survey questions distinguished between this and other center-based care outside of kindergarten hours. Three binary indicators were created. The first was whether a child attended center-based care in prekindergarten. The sec- ond was whether a child attended center-based care before/after school during the kindergarten school year. The third was whether a child attended center-based care during both prekindergarten and kindergarten years. Almost 70% of the sample attended center- based care in prekindergarten, as consistent with prior research using other national samples of child data (Loeb et al., 2007). Almost 20 percent of children attended center-based care outside of kindergarten during the kindergarten school year, as consistent with prior research (Claessens, 2012). Finally, 15% of the sample had attended care in both years. Other measures Entry skills Three sets of school entry skills were utilized, assessed at the start kindergarten. First a child’s item response theory-scaled scores on math and reading assessments were included. Second were five socioemotional scales, which were utilized in prior research using ECLS-K (e.g., Claessens, 2012). The scales were derived from the teacher’s assessment of child behavior. Based on the Social Skills Rating System (‘SSRS’; Gresham & Elliott, 1990), NCES modified these scales and created its own Teacher Social 92 164 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 Table 1 Descriptive statistics (N = 14,060). Center PK only Center in K only Center PK & Center in K Neither Mean SD Mean SD Mean SD Mean SD Chronic absenteeism 0.12 (0.33) 0.11 (0.32) 0.08 (0.26) 0.17 (0.38) Moderate 0.10 (0.29) 0.07 (0.27) 0.06 (0.23) 0.13 (0.33) Strong 0.03 (0.17) 0.05 (0.20) 0.02 (0.13) 0.05 (0.21) Skills at kindergarten entry Reading 36.07 (11.78) 32.97 (12.25) 37.45 (11.54) 31.96 (11.68) Math 30.44 (10.74) 27.65 (10.60) 31.94 (10.53) 26.69 (10.57) Self-control 3.12 (0.62) 2.98 (0.62) 3.00 (0.64) 3.08 (0.63) Interpersonal skills 3.02 (0.63) 2.90 (0.62) 2.94 (0.62) 2.97 (0.65) Approaches to learning 3.00 (0.67) 2.84 (0.65) 2.92 (0.67) 2.90 (0.69) Internalizing problem behaviors 1.44 (0.47) 1.49 (0.54) 1.45 (0.48) 1.48 (0.51) Externalizing problem behaviors 1.57 (0.61) 1.74 (0.64) 1.72 (0.66) 1.57 (0.61) Praises school 0.86 (0.36) 0.87 (0.36) 0.83 (0.37) 0.84 (0.37) Eager to attend school 0.86 (0.35) 0.86 (0.35) 0.82 (0.38) 0.84 (0.36) Student characteristics Male 0.52 (0.50) 0.52 (0.50) 0.52 (0.50) 0.51 (0.50) Black 0.11 (0.31) 0.14 (0.38) 0.17 (0.37) 0.14 (0.34) Hispanic 0.20 (0.40) 0.29 (0.44) 0.16 (0.36) 0.32 (0.46) Asian 0.04 (0.26) 0.03 (0.24) 0.04 (0.27) 0.03 (0.23) Other 0.05 (0.23) 0.07 (0.23) 0.07 (0.26) 0.06 (0.25) Has disability 0.22 (0.41) 0.28 (0.43) 0.21 (0.40) 0.19 (0.38) English language learner 0.11 (0.34) 0.13 (0.35) 0.05 (0.26) 0.21 (0.41) Kindergarten entry age (months) 66.31 (4.59) 65.82 (4.92) 65.80 (4.70) 66.02 (4.78) Health 1.58 (0.80) 1.71 (0.83) 1.53 (0.75) 1.65 (0.85) Household characteristics Parents married 0.69 (0.46) 0.50 (0.50) 0.68 (0.47) 0.59 (0.49) Number of siblings 1.52 (1.08) 1.54 (1.25) 1.11 (0.91) 1.65 (1.25) Age of mother at first birth 24.45 (5.75) 22.36 (5.47) 25.87 (6.20) 22.52 (5.23) Number of children’s books at home 95.61 (145.88) 77.23 (146.43) 96.02 (143.63) 68.71 (90.80) Distance from school 5.01 (4.25) 5.15 (6.32) 5.20 (3.45) 4.99 (4.69) Number of places child has lived 1.99 (1.20) 2.36 (1.41) 2.02 (1.20) 2.11 (1.25) Mother reported depression 0.19 (0.39) 0.26 (0.44) 0.20 (0.39) 0.24 (0.42) Learning activities 2.99 (0.45) 2.88 (0.48) 2.95 (0.43) 2.94 (0.50) Parental involvement 3.65 (2.55) 2.89 (2.41) 3.78 (2.58) 2.82 (2.41) Mother’s education Less than high school 0.10 (0.30) 0.18 (0.38) 0.04 (0.20) 0.21 (0.41) High school diploma or GED 0.20 (0.40) 0.30 (0.45) 0.15 (0.35) 0.28 (0.45) Some college 0.34 (0.47) 0.34 (0.48) 0.33 (0.47) 0.33 (0.47) College graduate or beyond 0.36 (0.48) 0.18 (0.40) 0.48 (0.50) 0.19 (0.40) Father’s education Less than high school 0.12 (0.31) 0.18 (0.39) 0.07 (0.22) 0.22 (0.40) High school diploma or GED 0.27 (0.43) 0.34 (0.47) 0.24 (0.41) 0.33 (0.47) Some college 0.28 (0.44) 0.32 (0.46) 0.28 (0.44) 0.27 (0.44) College graduate or beyond 0.33 (0.49) 0.16 (0.38) 0.41 (0.50) 0.18 (0.41) Household income 68,130.36 (54,556.58) 51,687.44 (50,066.03) 81,997.89 (59,222.46) 48,177.77 (45,581.48) Mother works full time 0.31 (0.46) 0.47 (0.50) 0.63 (0.48) 0.28 (0.44) Mother works part time 0.29 (0.45) 0.25 (0.42) 0.26 (0.44) 0.24 (0.43) Mother does not work 0.40 (0.49) 0.28 (0.45) 0.11 (0.30) 0.48 (0.50) Other child-care measures Non-center non-parental preK care 0.10 (0.30) 0.24 (0.43) 0.12 (0.33) 0.09 (0.28) Non-center non-parental K care 0.08 (0.27) 0.06 (0.24) 0.06 (0.23) 0.07 (0.24) Hours of all non-parental preK care 16.78 (10.80) 13.63 (17.51) 24.96 (13.91) 8.40 (14.83) Hours of all non-parental K care 4.51 (9.64) 10.42 (6.69) 11.20 (7.68) 4.99 (10.76) Center-based care before preK 0.56 (0.50) 0.24 (0.43) 0.53 (0.50) 0.11 (0.30) Full-day kindergarten 0.82 (0.39) 0.88 (0.33) 0.82 (0.39) 0.82 (0.39) % of sample 0.50 0.02 0.15 0.33 Rating Scales (SRS) in ECLS-K:2011. All scales were continuous on a 4-point Likert metric, with higher scores indicating more frequent behavior. All scales had high internal consistency, with the alpha reliability coefficients ranging from 0.79 to 0.91, as noted in the user’s manual (Tourangeau et al., 2013). The 4-item self control scale (˛ = 0.81) measured the extent that the child was able to control his or her temper, respect others’ property, accept his or her peers’ ideas, and handle peer pressure. The 5-item interpersonal skills scale (˛ = 0.86) was the frequency by which a child was able to get along with others, form and maintain friendships, help other children, show sensitivity to the feelings of others, and express feelings, ideas, and opinions in pos- itive ways. The 7-item approaches to learning scale (˛ = 0.91) was the frequency that the child was able to keep his or her belongings organized, show eagerness to learn new things, adapt to change, persist in completing tasks, pay attention, and follow classroom rules. The 5-item externalizing behaviors scale (˛ = 0.88) was the frequency with which a child argue, fought, got angry, acted impul- sively, and disturbed ongoing activities. The 4-item internalizing behaviors scale (˛ = 0.79) was the extent that the child exhibited anxiety, loneliness, low self-esteem, and sadness. As this study addressed school-going behavior, two additional entry skills were included. In the fall survey, parents rated the fre- quency with which their child praised school: a binary measure was created, indicating if praising school occurred more than once per week. Parents rated the frequency with which their child expressed 93 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 165 eagerness to attend school. Again, a binary measure was created, indicating if expressing eagerness occurred more than once per week. Student characteristics Student characteristics include a common set of measures, such as gender, race, having a disability, and English language learn- ing status. Additional key measures are also included. First, age of kindergarten entry is included, as prior research has indicated that younger kindergarten entrants may have a more difficult tran- sition into kindergarten due to younger age (Datar, 2006; Elder & Lubotsky, 2009). Second, ECLS-K asked parents to rate their child’s health on a five-point scale (1 was highest, 5 was lowest). Students who are less healthy have greater absences (Allen, 2003; Bloom, Dey, & Freeman, 2006). Household characteristics ECLS-K:2011 included a wide span of family variables, which are used to account for selection into center-based care. This study included measures for whether parents were married, the number of siblings, age of mother when she first gave birth to any child, and the number of books at home. Also, Gottfried (2010) found a link between absenteeism and distance from school, and thus this was included as a measure. Student mobility has also been linked to absenteeism (Chang & Romero, 2008; Ready, 2010) and was included. Claessens et al. (in press) linked maternal depression to absenteeism. A binary measure reported if a mother reported feeling depressed in the week prior to the survey. Two home involvement scales were also employed, as replicated from Votruba-Drzal, Li-Grining, and Maldonado-Carreno (2008). The first scale, which was comprised of 15 dichotomously-scored items, measured the number of learning activities in which chil- dren participated. This scale assessed whether in the past month, the child engaged in activities such as visited a book store, took music lessons, or attended tutoring lessons. The second scale, relat- ing to parental involvement, was measured on a 4-point Likert scale. The 10-item parental involvement scale assessed the fre- quency that parents engaged the child in various activities, such as playing games, singing songs, reading books, and doing arts and crafts. Finally are measures of socioeconomic status. Both maternal and parental education are included. Additional measures were house- hold income and maternal employment. Other child-care measures Two indicators designated whether a child received non- parental, non-center care during the prekindergarten year (such as relative care and/or non-relative care) and during the kindergarten year. Also, the number of hours of all types of non-parental care in both prekindergarten and kindergarten were included. Next was a binary measure for attending center-based care prior to prekinder- garten. Finally was an indicator for attending full-day kindergarten. With more absences occurring in kindergarten because students and families are not yet acclimated to the schooling schedule (Balfanz & Byrnes, 2012; Chang & Romero, 2008), this might be less of a concern in a full-day schedule. Analytic approach Baseline approach This study began with a baseline logistic regression model: prob[CAik ] = prob[CAik = ˇ0 + ˇ1 CBCik + ˇ2 Eik + ˇ3 Sik + ˇ4 Fik + ˇ5 Cik + εik ] > 0. In this model, CAik represented the binary outcome as to whether a child i in school k was a chronically absent. Given that CAik was binary, ordinary least squares was not appropriate (Cameron & Trivedi, 2010). The model was run separately for each chronic absence measure – first for the comprehensive measure and then for moderate and strong. In this way, there were three models run for assessing the effect of center-based care: each was designated by chronic absenteeism outcome. It was tested as to whether the models for moderate and strong chronic absenteeism were statistically different from one another. Using a seemingly unrelated regression postestimation test, the findings suggested that these models were indeed different at the p < 0.01 level. Thus, moderate and strong models are presented separately in the tables going forward. CBCik represented all center-based care indicators: prekinder- garten, before/after school in kindergarten, and both years. Eik represented entry skills, Sik represented child characteristics, Fik represented family characteristics, and Cik represented other care measures. The error term was school clustered to account for the non-independence of individual observations. Thus, clustering stu- dent data at the school provided for a corrected estimate of the variance of the error. Fixed effects modeling It might have been the case that omitted variable biases per- sisted in this model. A first attempt at reducing any omitted variable bias coming through at the school, county, or state levels was through a fixed effects strategy: the mechanics of such an approach are described in detail in Schneider, Carnoy, Kilpatrick, Schmidt, & Shavelson (2007) and in other studies on absenteeism using ECLS-K (e.g., Gershenson, Jacknowitz, & Brannegan, 2014). Three fixed effects approaches were employed. First were school fixed effects to account for possible unobserved school-level influ- ences on going to center-based care and on chronic absenteeism. For example, some schools might have highly-involved principals, though unobserved to the researcher. Here, highly-involved prin- cipals might find ways (e.g., working with the PTA, revising school budget, etc.) to introduce before/after school care for children in kindergarten. Therefore, in these schools, the probability of being a child who attended center-based care before/after school in kinder- garten might be higher than in other schools. At the same time, highly-involved principals might make additional investments to reduce school absences in kindergarten. Without measuring all principal efforts, the estimate of the variables in 1 would be sys- tematically biased. To address this, a school fixed effects approach was employed: prob[CAik ] = prob[CAik = ˇ0 + ˇ1 CBCik + ˇ2 Eik + ˇ3 Sik + ˇ4 Fik + ˇ5 Cik + ık + εik ] > 0. In this model, ık represents school fixed effects for children in school k. This term represents a set of binary variables that indicates if a child had attended a particular school. This set of indicator vari- ables leaves out one school as the reference group. School fixed effects held constant all school-to-school variation by conducting a within-school analysis: common but unobservable factors among children in the same school were held constant. Note that all school variables (and any that would be at a higher level) dropped away with school fixed effects. Following school fixed effects were county and state fixed effects. The results were similar between school and county and state fixed effects. Thus, the description of the two approaches and their findings can be found in the online supplementary material for this journal. 94 166 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 Propensity score matching Another concern might be the selection by families of their children into center-based care. As it stands so far, all students iden- tified as having been in center-based care are compared to all other students in the sample. A restricted control group might make for a more accurate comparison. To do so, propensity matching was employed. Based on the fact that out of the three key measures only prekindergarten was statistically significant in the baseline and school fixed effects reuslts to follow, students were matched on the propensity of having attended center-based prekindergarten care. As ancillary tests, the models were rerun twice (first on the propensity to having attended center-based kindergarten care; sec- ond on the propensity to having attended center-based care in both years). These results were also consistent with the baseline and fixed effects, namely a lack of significant effects. In a two-stage procedure, students from the treatment (i.e., center-based care in prekindergarten) were matched to a con- trol group (i.e., no center-based care in prekindergarten) based on observable characteristics. Note that in the propensity score models in which the treatment was center-based care attendance in prekindergarten, all variables that could have been affected by this treatment were required to be excluded (e.g., having attended center-based kindergarten, having attended both years, hours of care). Thus, the full set of predictors utilized in the first stage of the propensity score analysis included: gender, race, disability status, non-English was primary spoken language, parent-rated health, parental marital status, number of siblings, age of mother at first birth, number of books at home, distance from school, number of places the child has lived, maternal depression, home learn- ing activities, parental involvement, parental education, household income, employment status, and center-based care attendance prior to prekindergarten. In the first stage, the propensity score was calculated for selec- tion into center-based prekindergarten care. The propensity score was the conditional probability that a child with a set of observ- able characteristics was in center based-care. The propensity score was estimated using logistic modeling (Rosenbaum & Rubin, 1983). The second stage used the propensity scores from the first stage to match children who were and were not in center-based care. The difference between the outcomes for these two groups was the average treatment effect. The matching method employed was one-to-one nearest-neighbor matching without replacement (Rubin, 1973). Any control group observations that did not have a match were discarded. In doing so, the distribution of the observ- able characteristics between children attending and not attending center-based prekindergarten care were much more similar and allowed for a more refined comparison (Dehejia & Wahba, 2002). Results Baseline models Table 2 presents the findings from the baseline logistic models. Each model presented in the table is unique – the binary dependent variable is indicated by column heading. The key predictors are located in the first section of the rows. The coefficients are odds ratios (with standard errors clustered by school in parentheses). A larger value of the coefficient suggests a worse outcome – a higher odds of being a chronic absentee. A more favorable outcome occurs with lower coefficient values, which indicates a lower odds of being a chronic absentee. Across all models, the results in Table 2 indicate that going to center-based prekindergarten care was associated with lower odds of being a chronic absentee in kindergarten. In more detail, having attended center-based prekindergarten care was associ- ated with odds of 0.80-to-1 that a child was chronically absent, as indicated in the first column. When chronic absenteeism was eval- uated through its alternative definitions, similar patterns emerged. Having attended center-based prekindergarten care, children had lower odds of moderate chronic absenteeism in kindergarten (0.84-to-1) and even lower odds of strong chronic absenteeism (0.75-to-1). Odds ratios were translated into effect sizes per Cox (1970) and What Works Clearinghouse (NCES, 2014). The effect size of prekindergarten care for the overall measure of chronic absenteeism was 0.13 and was 0.10 for moderate and 0.17 for strong outcomes. These effects were consistent with (or slightly larger) other assessments of center-based care using secondary data (Bassok, 2010; Claessens, 2012; Loeb et al., 2007; Turney & Kao, 2009; Yamauchi & Leigh, 2011), though this was the first study addressing absence outcomes. As for research question two, neither having attended center- based care before/after school in the same year as kindergarten nor having attended center-based care during both prekindergarten and kindergarten years was statistically significant. Therefore, the relationship between center-based care and chronic absenteeism in kindergarten was driven specifically by care in the year just before kindergarten. This finding is also reinforced by the fact that the indicator for having attended center-based care prior to prekindergarten (near the bottom of the table) was not significant in predicting chronic absenteeism in kindergarten. Again, the rela- tionship between center-based prekindergarten care and chronic absenteeism was unique. Overall, there are several key interpretations. First, children in center-based care in prekindergarten had lower odds of chronic absenteeism compared to children not in center-based prekinder- garten care. Second, the interpretation of all three models was consistent. Distinguishing between absence definitions did not dra- matically change the conclusion. Finally, looking across all models, statistically-significant odds of center-based care only arose for prekindergarten care. Only attending prekindergarten care reduced chronic absenteeism. Briefly turning to the wide span of control variables imple- mented in this study, kindergarten entry skills and individual characteristics were generally not associated with differences in the odds of chronic absenteeism. One interesting exception, how- ever, was that children with higher frequencies of internalizing and externalizing behaviors tended to have higher odds of chronic absenteeism. This finding corresponds to previous research, sug- gesting that feelings of anxiety, disengagement, or alienation are linked to higher rates of missing school (Ekstrom et al., 1986; Newmann, 1981). Unsurprising, health was a consistently strong predictor of chronic absenteeism. Children with lower health rat- ings (and presumably poorer health) were more likely to be chronically absent. Children in poorer health tend to be at the high- est levels of risk for chronic absenteeism (Allen, 2003; Bloom et al., 2006). Health is such an important factor that children in schools without health personnel tend to have greater absences (Allen, 2003). There were several notable findings from the set of household characteristics. Children with siblings were less likely to be chronic absentees. Children in households that have been more mobile had higher odds of chronic absenteeism, as supported by the literature (Felner, Primavera, & Cauce, 1981). One intriguing result was that children with parents who are highly involved in learning activi- ties had higher odds of being chronic absentees. While speculation, highly-involved parents might allow for their child to be absent more by assuming that they could supplement school material at home through their involvement. Consistent with Claessens et al. (in press), maternal depression was linked to higher odds of chronic absenteeism. Note that maternal depression has been linked to 95 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 167 Table 2 Center-based care and the odds of chronic absenteeism. Alternative definitions Chronic absenteeism Moderate Strong Center-based care Prekindergarten center-based care 0.80 (0.07)***0.84 (0.08)*0.75 (0.08)* Kindergarten center-based care 0.64 (0.15) 0.60 (0.16) 0.84 (0.32) Both 1.05 (0.27) 1.13 (0.33) 0.89 (0.39) Skills at kindergarten entry Reading 0.99 (0.00)*0.99 (0.01) 0.99 (0.01) Math 1.00 (0.00) 1.00 (0.01) 1.00 (0.01) Self-control 1.09 (0.11) 1.03 (0.11) 1.23 (0.23) Interpersonal skills 1.02 (0.09) 1.06 (0.10) 0.91 (0.15) Approaches to learning 0.77 (0.07)**0.80 (0.07)*0.77 (0.11) Internalizing problem behaviors 1.23 (0.08)***1.14 (0.08) 1.36 (0.14)** Externalizing problem behaviors 0.82 (0.08)*0.79 (0.07)**0.94 (0.13) Praises school 0.89 (0.09) 0.89 (0.10) 0.96 (0.18) Eager to attend school 0.95 (0.09) 0.93 (0.09) 1.02 (0.19) Student characteristics Male 0.93 (0.06) 1.04 (0.07) 0.73 (0.09)** Black 0.89 (0.10) 0.81 (0.10) 1.17 (0.21) Hispanic 0.91 (0.09) 0.92 (0.09) 0.94 (0.18) Asian 1.16 (0.19) 0.91 (0.18) 1.88 (0.48)* Other 1.39 (0.16)**1.25 (0.16) 1.60 (0.31)* Has disability 1.14 (0.10) 1.09 (0.11) 1.20 (0.22) English language learner 0.69 (0.10)**0.69 (0.10)**0.77 (0.18) Kindergarten entry age 0.99 (0.01) 1.00 (0.01) 0.99 (0.01) Health 1.23 (0.05)***1.15 (0.05)***1.35 (0.09)*** Household characteristics Parents married 0.86 (0.07) 0.85 (0.08) 0.94 (0.13) Number of siblings 0.89 (0.03)***0.92 (0.03)**0.87 (0.05)** Age of mother at first birth 0.97 (0.01)***0.98 (0.01)*0.96 (0.02)* Number of children’s books at home 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) Distance from school 1.02 (0.01)**1.02 (0.01)**1.02 (0.01) Number of places child has lived 1.06 (0.03)*1.03 (0.03) 1.12 (0.05)* Learning activities 1.37 (0.10)***1.29 (0.10)**1.45 (0.20)** Parental involvement 0.98 (0.01) 1.00 (0.02) 0.92 (0.02)** Mother’s reported depression 1.23 (0.10)*1.10 (0.11) 1.49 (0.17)*** Mother’s education Less than high school 1.08 (0.12) 1.01 (0.13) 1.20 (0.23) Some college 0.92 (0.08) 0.98 (0.09) 0.80 (0.13) College graduate or beyond 0.87 (0.10) 0.83 (0.11) 1.03 (0.21) Father’s education Less than high school 0.95 (0.12) 1.08 (0.14) 0.70 (0.14) Some college 0.94 (0.08) 0.87 (0.09) 1.14 (0.16) College graduate or beyond 1.04 (0.12) 1.01 (0.12) 1.11 (0.22) Household Income 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) Maternal employment Full time 0.69 (0.06)***0.78 (0.07)**0.57 (0.10)*** Part time 0.78 (0.06)***0.85 (0.08) 0.67 (0.10)* Other child-care measures Non-center non-parental preK care 0.75 (0.11) 0.75 (0.12) 0.82 (0.21) Non-center non-parental K care 0.84 (0.14) 0.76 (0.14) 1.10 (0.31) Hours of all non-parental preK care 1.00 (0.00) 1.00 (0.00) 1.00 (0.01) Hours of all non-parental K care 1.01 (0.00) 1.01 (0.00) 1.01 (0.01) Center-based care before preK 1.03 (0.08) 1.06 (0.09) 0.94 (0.12) Full-day kindergarten 1.18 (0.13) 1.21 (0.14) 1.05 (0.17) n 14,060 14,060 14,060 Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Robust standard errors adjusted for clustering in parentheses. children’s internalizing behaviors, and childcare attendance reduces this association (Hebra et al., 2013). Children with work- ing mothers had lower odds of chronic absenteeism. Other care measures were not statistically significant. Fixed effects Thus far, the logistic regression models included a wide range of control measures that might have confounded the relationship between going to center-based care and chronic absenteeism. To account for unobserved school influences that may have influenced going to center-based care as well as the odds of chronic absen- teeism, the original baseline logistic regression models from Table 2 were modified to include school fixed effects. Table 3 presents the odds ratios and clustered standard errors from baseline, school fixed effects, and propensity models. The first section presents odds ratios and standard errors for the models where the outcome was general chronic absenteeism, the second section for moderate chronic absenteeism as an outcome, and the third for strong chronic absenteeism. This portion of analysis focuses specifically on comparing base- line and school fixed effects models. Both models include the set of control variables from Table 2. Note that the sample sizes changed based on variation in the outcomes at the level of the fixed effect specification (e.g., schools lacking variation would be dropped). Examining the fixed effects models and comparing them with the baseline, the results are consistent. The sizes of the odds as well as the standard errors are similar in each regression. This indicates 96 168 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 Table 3 Comparison of alternative specifications. Key covariates Prekindergarten care Kindergarten care Both n Outcome: chronic absenteeism Baseline (Table 2) model 0.80 (0.07)***0.64 (0.15) 1.05 (0.27) 14,060 School fixed effects model 0.71 (0.08)***0.58 (0.15) 1.13 (0.32) 11,480 Propensity score matchinga 0.85 (0.27)***– – 11,740 Outcome: moderate chronic absenteeism Baseline (Table 2) model 0.84 (0.08)*0.60 (0.16) 1.13 (0.33) 14,060 School fixed effects model 0.78 (0.09)*0.61 (0.17) 1.12 (0.34) 11,480 Propensity score matchinga 0.85 (0.27)***– – 11,740 Outcome: strong chronic absenteeism Baseline (Table 2) model 0.75 (0.08)*0.84 (0.32) 0.89 (0.39) 14,060 School fixed effects model 0.64 (0.14)*0.66 (0.28) 1.04 (0.53) 11,480 Propensity score matchinga 0.80 (0.27)***– – 11,740 Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Robust standard errors adjusted for clustering in parentheses. a As described in the text, propensity score models were matched on center-based prekindergarten attendance. ‘Kindergarten care’ and ‘both’ covariates were thus not included in the matching algorithm. that there was either no bias at the school level or alternatively that the fixed effects approach did not identify the most likely source of bias, which probabilistically occurred due to family selec- tion (hence the use of a matched design to follow). While there were some minor differences, nothing veers from the interpreta- tion: center-based prekindergarten care linked to lower odds of chronic absenteeism. Propensity score matching While the analysis has included a rich set of measures and fixed effects, there was nonetheless the possibility that children (fami- lies) in center-based care were different in some fundamental way compared to those not in center-based care. Given the statistically- significant findings for attending center-based prekindergarten care, a propensity score matching design was implemented to match children who did and did not attend center-based prekinder- garten care. In doing so, propensity matching was useful in creating a more well-defined comparison group. Table 4 presents standardized mean differences for the covari- ates utilized in the matching algorithm between children who did and did not attend center-based prekindergarten care. The first column presents the standardized mean differences for the full, original sample. The second column presents standardized mean differences only for those sample members who had a matched treatment-control pairing. The measures running down the right- hand side of the table list all variables included in the matching algorithm. Note, however, that including entry-level skills did not change the findings, though they were not included in this final model as they were technically measured after having attended center-based prekindergarten. Comparing the columns, the stan- dardized mean difference on the variables used in the first phase of the propensity matching analysis were reduced to |.10| or less. This stands in contrast to the standardized mean differences on many of the variables prior to having matched children. The original results were re-examined and are presented in Table 3 below the school fixed effects results. The propensity score matching results were close in size compared to the original results (though they often held greater level of statistical significance). The results from the propensity matching analysis suggested a slight overestimation of the previously-estimated low odds associated with center-based prekindergarten on chronic absenteeism – as indicated by odds for all three outcomes. Family selection was slightly overestimating the prior sets of results, which were not picked up by the baseline model nor by the fixed effects models. That said, this overestimation was small, only moving the odds up by five percentage points for chronic absenteeism, one percentage Table 4 Standardized mean differences on all first stage predictors in the propensity score analysis. Original sample (n = 14,060) Matched sample (n = 11,740) Male 0.02 0.02 Black −0.03 −0.03 Hispanic −0.26 −0.01 Asian 0.05 0.05 Other −0.01 −0.01 Has disability 0.07 0.07 English language learner −0.25 −0.09 Health −0.10 −0.10 Parents married 0.22 0.08 Number of siblings −0.20 0.07 Age of mother at first birth 0.41 −0.03 Number of children’s books at home 0.21 −0.07 Distance from school 0.04 0.04 Number of places child has lived −0.11 −0.10 Mother reported depression −0.11 −0.10 Learning activities 0.08 0.08 Parental involvement 0.35 0.06 Mother: less than high school −0.36 −0.05 Mother: some college 0.02 0.02 Mother: college graduate or beyond 0.44 −0.10 Father: Less than high school −0.26 −0.05 Father: some college 0.04 0.02 Father: college graduate or beyond 0.37 −0.02 Household income 0.46 −0.07 Mother: full time employment 0.23 −0.08 Mother: part time employment 0.09 0.09 Attended center-based care prior to prekindergarten 0.91 0.03 points for moderate, and five percentage points for strong. There- fore, the findings from this model nonetheless conclude with the same interpretation: children who attended center-based prekindergarten care had lower odds of chronic absenteeism. Heterogeneity Previous research has suggested that SES moderates attendance patterns (as well as the effect of center-based care on other child outcomes, such as achievement or socioemotional development). Hence, to be comprehensive in this study, SES characteristics were examined. Recent policy dialog in chronic absenteeism for low-SES children has supported a movement beyond examining traditional SES measures such as receiving free lunch (Nauer et al., 2014). To align with this effort, various measures of socioeconomic status were tested. 97 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 169 Table 5 Analysis by socioeconomic status. Alternative definitions Chronic absenteeism Moderate Strong Low health rating × Prekindergarten center-based care 1.04 (0.19) 0.97 (0.19) 1.35 (0.43) Kindergarten center-based care 0.97 (0.54) 0.24 (0.26) 5.12 (3.54) Both 0.83 (0.52) 3.06 (3.44) 0.21 (0.19) ECLS-K poverty indicator × Prekindergarten center-based care 1.22 (0.22) 1.17 (0.23) 1.35 (0.43) Kindergarten center-based care 0.15 (0.10) 0.13 (0.11) 0.25 (0.23) Both 6.42 (4.49) 8.02 (7.04) 3.19 (3.56) Family received food stamps × Prekindergarten center-based care 1.24 (0.19) 1.21 (0.21) 1.41 (0.39) Kindergarten center-based care 0.61 (0.27) 0.28 (0.17) 2.96 (2.10) Both 1.58 (0.79) 3.63 (2.39) 0.26 (0.22) Mother has less than high school degree × Prekindergarten center-based care 0.94 (0.18) 1.17 (0.25) 0.56 (0.18) Kindergarten center-based care 1.77 (0.91) 1.49 (0.92) 1.74 (1.41) Both 0.71 (0.44) 0.73 (0.57) 0.86 (0.82) Mother does not work × Prekindergarten center-based care 0.86 (0.13) 0.75 (0.13) 1.40 (0.38) Kindergarten center-based care 1.12 (0.50) 0.86 (0.48) 1.94 (1.21) Both 0.94 (0.51) 1.29 (0.85) 0.42 (0.34) Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Robust standard errors adjusted for clustering in parentheses. To test for moderating effects, partially-interacted models were employed where indicators for center-based care were interacted with a measure of SES. Utilizing partially-interacted models is deemed as appropriate for assessing heterogeneity in childcare effects delineated by a child-level characteristic (Yamauchi & Leigh, 2011). Hence, it was the approach also adopted here. Table 5 presents these moderating effects. Each grouping represents a unique regression. The odds ratio and school-clustered standard errors were derived from running a logistic regression model sim- ilar to that in Table 2. The sections of Table 5 present interactions between center-care measures and different SES measures, includ- ing indicators for: fair or poor parental health rating, family was at/or below poverty level, received food stamps assistance in the 12 months prior to kindergarten entry, child’s mother did not com- plete high school (note that the results were the same when using father’s education), and maternal unemployment. Across all measures, there was no statistical significance. Thus, children of varying degrees of SES did not experience differences in the odds of chronic absenteeism based on attending center-based care. The lack of statistical significance of effects by SES were con- sistent with recent research on center-based care on other child outcomes (Claessens, 2012). Instead, when considering SES, all chil- dren benefited (equally) from having attended center-based care. Note that other moderating characteristics were tested as well. Those selected were those that were statistically-significant find- ings in Table 2, such as: gender, living close to school, and degree of home learning activities. There were no moderating effects of these on chronic absenteeism. Instead, only the direct, main effect of center-based prekindergarten care emerged. Discussion This study was the first to position itself in the intersection on research on center-based care and on chronic absenteeism. Given the growth in the utilization of center-based care, this study con- tributed the body of research focusing on early schooling outcomes of children in these programs. Given recent policy concerns of the detrimental effects of chronic absenteeism in early education (see Balfanz & Byrnes, 2012; Chang & Romero, 2008; Gottfried, 2014; Nauer et al., 2014), this present study also contributed new research to the field of school absenteeism by examining whether attending center-based care influenced absences. With increased enrollment in center-based care alongside increased concerns of the detrimen- tal effects of chronic absenteeism, this study addressed a critical research gap in order to draw conclusions about both. To do so, this study relied on the most recent national dataset of school-aged children in the U.S. – the ECLS-K:2011. Given these data, it was possible to incorporate a wide span of measures and methods in the analyses to better isolate the association between going to center-based care and chronic absenteeism. Importantly, prior to this study, little work had focused on attending center- based care before/after school during the kindergarten school year (Claessens, 2012). However, the ECLS-K:2011 dataset uniquely pro- vides information on care experiences in both prekindergarten and kindergarten years, allowing this study to evaluate how the timing of center-based care influenced chronic absenteeism. This new per- spective was critical, given the growing national trend of children attending both prekindergarten and kindergarten center care. Addressing each research question led to the following con- clusions. As for the first, children in center-based prekindergarten care had lower odds chronic absenteeism in kindergarten. These relationships held true regardless of absenteeism definition. The findings were robust to multiple methodological approaches, though propensity matching suggested a slight overestimation in prior models hence elucidating the value of a matched design. Based on the mechanisms addressed in the introduction, it does seem feasible that center-based prekindergarten care enables for the development of school-going skills that ease the transition into schooling for both children and parents. In a formal school-like setting, children build skills to adapt to the transition into school- ing and consequently develop positive school-going attitudes once in kindergarten (Ladd & Price, 1987). Positive attitudes, which as previously mentioned, are inversely related to school avoidance and absenteeism (Ekstrom et al., 1986; Gottfried, 2014; Newmann, 1981). Parents might be in a better position to develop schedules and logistics prior to starting kindergarten, which reduce stress and anxiety once their children are start school (Ehrlich et al., 2014) – and again positive school-going behaviors are developed. Maintaining routines seems critical for families to reduce chronic absenteeism, as highlighted by the statistically-significant effect of family mobility. A third explanation might be the health benefits of early exposure to health programs (Yoshikawa et al., 2013). As for research question two, only the measure of having gone to center-based care during prekindergarten significantly 98 170 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 predicted differences in odds in chronic absenteeism. No other center-based care measure was significant. In the year prior to schooling, center-based care would be the only exposure children would have to a formal school-like environment. On the other hand, as Claessens (2012) describes, center-based care before/after the kindergarten day may not add significant value above-and-beyond the school-going and transition skills children are already acquiring in kindergarten itself. Therefore, the importance of center-based care on chronic absenteeism might be driven by the mechanisms described in the introduction pertaining to prior-to-kindergarten center-based care rather than as concurrent reinforcement. As for the third research question, the results were compa- rable between various measures of SES, as consistent with prior research (Claessens, 2012). The relationship between center-based prekindergarten care and chronic absenteeism was not accentu- ated for one group. Rather, all students were positively influenced by having attended prekindergarten center-based care. The null findings by SES has important research implications. A limitation of ECLS-K:2011 is that center quality was not directly observed or measured. Families of higher SES who send their chil- dren to center-based care might be sending them to ‘higher quality’ center-based care on some unobserved measure. Therefore, in examining moderating effects by SES, any differentiation in qual- ity might have been reflected in statistically-significant results by these moderating effects. These moderating results, however, were not significant, thereby implicating that at least these rough proxies for quality did not skew the results. From these findings, there are several concluding implications. First, less is known about the influence of center-based care on outcomes such as chronic absenteeism, which has recently been supported as a key indicator of academic risk (Nauer et al., 2014). Therefore, this study has explored a relatively new facet of how center-based care links to school success beyond achievement and development. Having done so enabled for a richer discussion as to why center-based care might be linked to achievement or socioemotional development in the first place. For instance, chronic absenteeism reduces achievement and weakens socioemotional development in kindergarten (Gershenson et al., 2014; Gottfried, 2014). As this study has shown, attending center-based prekinder- garten care linked to lower chronic absenteeism. Therefore, one path by which center-based care links to achievement or socioemo- tional development may be through chronic absenteeism – namely that center-based prekindergarten care increases positive school- going behaviors, reduces chronic absenteeism, and hence increases achievement and socioemotional development. This study encour- ages further exploration down this line as well as new research to consider additional indicators of child success and risk. Second, moving beyond identifying a standard set of individual and family factors, this study was also unique is that it broad- ened the dialog on what drives chronic absenteeism. The fact that both moderate and strong chronic absenteeism were influenced by center-based prekindergarten care suggests that stakeholders might consider how early childhood programs link to school-going behaviors and attitudes. The findings of this study urge for further documentation and exploration of the link between early child- hood experiences and early chronic absenteeism, such that policy and practice supports those factors and environments that promote positive school-going behaviors. Hence, the first two implications of this research are intertwined. On the one side, identifying how center-based care links to chronic absenteeism contributes new knowledge as to how center-based care influences a wider span of early schooling outcomes; and on the flip-side, knowing this rela- tionship also appeals to those invested in identifying what may be driving chronic absence patterns and how to mitigate them. While this study focused on center-based care and chronic absenteeism, other factors nonetheless emerged as significant. First, almost unsurprising was the role of child health (Allen, 2003; Bloom et al., 2006). The fact that health predicted chronic absen- teeism implies that child health ought to remain at the forefront of policy when it comes to curtailing early absences, through access to health care services and federal health programs (Zhang, 2012). The fact that SES did not moderate the relationship between center- based care and chronic absenteeism suggests that the operative conditions might be more related to health issues than to tradi- tional poverty issues. Second, a noteworthy set of findings arose for the relationships between maternal health and chronic absen- teeism. First, there was a main effect of maternal depression on school absenteeism as consistent with Claessens et al. (in press), thereby stressing the importance of mental health in boosting the family’s ability to establish and maintain routines. Second, there was an indirect effect of maternal health on absenteeism. Maternal depression is linked to children’s internalizing symp- toms (Hebra et al., 2013). Internalizing symptoms were found to be linked to chronic absenteeism in this study. Prior work has found that attending center-based care reduces the link between maternal depression and children’s internalizing symptoms (Hebra et al., 2013). Interpreting both main and indirect effects together, attending center-based childcare seems to be especially critical for children in families facing mental health issues, though it might be those same issues that are reducing the odds of attending center- based care to begin with. If parental mental health is linked to child mental health (and both directly link to early chronic absenteeism), future policy might focus on where and when on this pathway mental health supports and interventions are most effective to ensure that children go to prekindergarten and attend school once in kindergarten. Third, this study contributes new insight by considering if there is a role of multiple years of center-based care. However, given that significant effects only arose on prekindergarten care, researchers, policymakers, and practitioners might consider two avenues of inquiry. First, future questions could delve into which factors of prekindergarten care were distinct enough to have influenced chronic absenteeism in contrast to years of center-based care. Per- haps as Claessens (2012) suggests, center-based care before/after kindergarten hours was less effective (on achievement) because it simply reinforced what children were already learning during the actual kindergarten school day. Second, inquiry might address why multiple years of exposure to a program, policy, or practice was not effective at boosting early chronic absenteeism. Knowing which years are critical and which are not are crucial to develop- ing policy: The story remains incomplete when extrapolating early schooling outcomes based on one year of center-based care. Finally, in regards to the fact that this study focused on outcomes during this first year of formal schooling, there are implications. Early educational experiences can set students’ schooling and developmental trajectories over a lifetime (Duncan et al., 2007). As it has been established that chronic absence in early schooling years has the potential to influence children’s short- and long-term prospects (Gottfried, 2014), identifying how programs and prac- tices can potentially reduce chronic absenteeism may shed light on ways to provide young children at the onset of schooling with strong foundations to be successful throughout the educational pipeline. Limitations and further study In sum, this study provided new insight in the intersection on research in center-based care and on research in chronic absen- teeism. There are several avenues for future research grounded in limitations of this study. First, as mentioned, measures of qual- ity are important when evaluating child-care (Anders et al., 2011; Camilli, Vargas, Ryan, & Barnett, 2010; Vandell, Belsky, Burchinal, 99 M.A. Gottfried / Early Childhood Research Quarterly 32 (2015) 160–173 171 Steinberg, & Vandergrift, 2010). However, quality measures were not developed by NCES for the ECLS-K data. Thus, this study calls for additional research to delve into the moderating role of quality. This may be accomplished with other large-scale datasets (though at present, none are as recent as the one utilized in this study). Or, this may be accomplished through smaller-scale site studies. Second, absence reasons were not provided in the dataset. Thus, while it was possible to determine if a student was chronically absent, it is not possible to determine why. Thus, future research might rely on additional data to examine these relationships. For instance, district datasets contain coded reasons for absences. Therefore, future research can provide more depth into the care- absenteeism relationship, as well as test the generalizability of this study’s findings. Third, while the dataset included an exceptional array of child and household variables, the data were nonetheless non- experimental. Therefore, the fixed effects analyses only accounted for preexisting differences related to location (school, county, state) and the propensity score analysis only accounted for preexisting differences (e.g., family selection bias) on the variables included in the matching algorithm and not on other possible explanations. A smaller, experimental study would eschew these issues and could provide additional confidence in this study’s findings. A related lim- itation of this dataset was that its large size may have increased the possibility of finding statistically-significant effect sizes. Though the effect sizes were consistent with or larger than prior studies that also used non-experimental data to evaluate center-based care (e.g., Bassok, 2010; Claessens, 2012; Loeb et al., 2007; Turney & Kao, 2009; Yamauchi & Leigh, 2011), they were nonetheless smaller than those derived from experimental work on center-based care (e.g., Yoshikawa et al., 2013). Again, this urges for future experimental work to consider chronic absenteeism as an outcome in order to compare to the findings in this study. Fourth, this study raised potential mechanisms (e.g., transitions, family logistics, health) as to why center-based prekindergarten would influence chronic absenteeism, but it was not possible to identify the relative importance of one mechanism over the other. Further inquiry, perhaps through qualitative methods, could identify the critical pathways by which the relationship between center-based prekindergarten care and reduced chronic absen- teeism in kindergarten had arisen. In doing so, it will be possible to develop practices that target and support those mechanisms that seem to be strongest in linking center-based care and chronic absenteeism. For instance, knowing if family logistics is a critical issue in prekindergarten will aid in streamlining how to address the needs of children and their families. Finally, this study importantly evaluated the role of center- based care on early schooling outcomes. It would be as important to determine if there are longer-term effects of center-based care on chronic absenteeism, given the fact that this behavior is present across the K-12 pipeline. Therefore, with an appropriate set of data, future research could determine how center-based care sets chil- dren on both short- and long-term trajectories based on a range of critical outcomes. Appendix A. 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A study of the effect of medicaid coverage on school absenteeism. International Journal of Health Ser- vices, 42, 627–646. http://dx.doi.org/10.2190/hs.42.4.d 102 Frontiers in Education 01 frontiersin.org Why are children absent from preschool? A nationally representative analysis of Head Start programs Kelly M. Purtell * and Arya Ansari Department of Human Sciences and Crane Center for Early Childhood Research and Policy, The Ohio State University, Columbus, OH, United States Introduction: Children who are absent from school, including preschool, do not make the same academic gains as their non-absent peers. However, we know little about what predicts absenteeism among preschool-attending children. Methods: We used the Family and Child Experiences Study - 2009, a nationally representative sample of Head Start attendees (n = 2,842), to test the associations between a comprehensive set of child, family, and center factors, and children’s levels of absenteeism across the preschool year. Results: Our findings highlight the multi-faceted nature of absenteeism. Family necessity, family routines, and center-level characteristics were all associated with absenteeism. Discussion: Reducing preschool absenteeism requires a comprehensive approach as the factors that shape absences are varied. Our findings suggest that center-level strategies focused on outreach and classroom quality are important future directions. KEYWORDS absenteeism, preschool, Head Start, families, FACES 2009 Introduction Preschool is an effective means to improving children’s early learning and development, especially for children from low-income homes (Phillips et al., 2017). Given the mounting evidence supporting the benefits of preschool, large investments are being made into these programs across the country (Duncan and Magnuson, 2013). Despite these potential benefits of preschool enrollment, there is growing evidence to suggest that children do not reap the maximum benefit if they are not regularly present in school (Connolly and Olson, 2012; Ansari and Purtell, 2018; Ehrlich et al., 2018; Fuhs et al., 2018; Rhoad-Drogalis and Justice, 2018; Ansari et al., 2021). However, there has been little work on understanding why children are absent in the earliest years of schooling. To address this gap in scientific knowledge, we use a nationally representative sample of newly enrolled Head Start attendees to examine a comprehensive set of factors that TYPE Original Research PUBLISHED 16 December 2022 DOI 10.3389/feduc.2022.1031379 OPEN ACCESS EDITED BY Gil Keppens, Vrije University Brussel, Belgium REVIEWED BY Markus Klein, University of Strathclyde, United Kingdom Lilly Augustine, Jönköping University, Sweden *CORRESPONDENCE Kelly M. Purtell Purtell.15@osu.edu SPECIALTY SECTION This article was submitted to Educational Psychology, a section of the journal Frontiers in Education RECEIVED 29 August 2022 ACCEPTED 21 November 2022 PUBLISHED 16 December 2022 CITATION Purtell KM and Ansari A (2022) Why are children absent from preschool? A nationally representative analysis of Head Start programs. Front. Educ. 7:1031379. doi: 10.3389/feduc.2022.1031379 COPYRIGHT © 2022 Purtell and Ansari. This is an open- access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 103 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 02 frontiersin.org we hypothesize will be associated with children’s preschool absences. Understanding why children are absent in Head Start is an important policy question because it is the largest federally funded preschool program in the U.S., serving over 1 million children from low-income homes in 2019 alone (Office of Head Start, Administration for Children and Families, 2016). Head Start was created in 1965 as part of President Johnson’s War on Poverty with the goal of minimizing the socioeconomic disparities in children’s achievement. Interestingly, Head Start was designed as a two-generation program, meaning that it targeted both children and their parents (Zigler and Styfco, 2000). Thus, understanding how the family and school systems shape absenteeism in Head Start is particularly important. There is far less information available on the prevalence of absenteeism in preschool than formal schooling, but studies from Baltimore (Connolly and Olson, 2012) and Chicago (Ehrlich et al., 2018) suggest that absenteeism is especially high during the years leading up to kindergarten. For example, in Chicago, preschoolers were absent for roughly 10–13% of the school year (Ehrlich et al., 2018). These averages indicate that a large share of children were chronically absent, meaning they missed more than 10% of the school year. Results from preschool programs in Baltimore reveal even higher levels of chronic absenteeism, with almost 27% of children being chronically absent (Connolly and Olson, 2012). These high rates of absences is troublesome because: (a) children who are absent from preschool do not make the same academic gains as their classmates who are less frequently absent (Connolly and Olson, 2012; Ansari and Purtell, 2018; Ehrlich et al., 2018; Fuhs et al., 2018; Rhoad-Drogalis and Justice, 2018); and (b) the more often children are absent in the early years, the more likely they were to be absent later on (Connolly and Olson, 2012; Dubay and Hollar, 2016; Gottfried, 2017; Ansari and Pianta, 2019). But, overall, these findings are not entirely surprising; the K-12 literature has long highlighted the negative educational and financial implications of school absences (Gottfried, 2009, 2010, 2011; Ready, 2010; Gershenson et al., 2015; Gottfried and Hutt, 2019). To understand the consequences of absenteeism, and eventually to develop effective solutions to reduce it, requires a deeper understanding of the barriers to school attendance and which children are more likely to miss time from school. And even though there has been recent advances in trying to understand “why” children are absent in K-12 (e.g., Gottfried, 2015, 2017; Morrissey et al., 2014; Gottfried and Gee, 2017), there has been little effort to understand these dynamics among preschool-aged children. This is an important gap in knowledge because preschool differs from formal schooling in many ways that may contribute to both the higher rates of absenteeism and the reasons why children are absent. Primarily, preschool in the United States. is not mandated by law and because of its voluntary nature, some parents may view its role in promoting their children’s development differently than formal schooling. Indeed, while most parents believe that school attendance is important, there is variation in whether parents ascribe these beliefs in relation to preschool, or only when their children are older (Ehrlich et al., 2013). The potentially more varied beliefs about preschool may be one reason why there are higher levels of absences in preschool than later schooling (Dubay and Holla, 2015). When studying absenteeism, children’s health is often considered as the primary contributor to absenteeism (Ehrlich et al., 2013; however, there are multiple pathways that may lead to children missing school, especially in early childhood when schooling is not mandatory. Two theoretical models help to navigate this complex process. First, Bronfenbrenner’s bioecological theory highlights the importance of multiple contexts in shaping children’s development (Bronfenbrenner and Morris, 2006). Two tenets of this theory are particularly relevant. First, the concept of the mesosystem, which focuses on interrelations across contexts, highlights the interrelatedness of family and school environments. Applied here, it may be that experiences in the home prevent (or promote) children’s preschool attendance. Second, this framework also emphasizes the role of the child in shaping their own home and school experiences. Thus, it may be that when a child (or parent) has positive experiences at preschool, they may be more motivated to go (or send their child), and in turn, reduce absenteeism. The accommodations framework (Meyers and Jordan, 2006) also provides a useful lens through which to understand children’s school absences. This economic framework was initially developed to explain how families make choices about childcare for their children and highlights the complex web of factors that influence these choices. Many of these factors may also influence preschool attendance. For example, need and necessity are highlighted as key factors that shape parents’ decisions for childcare. When applied here, it may be that families with working parents have children with fewer absences because they need childcare so they can work; however, for families with mismatches between employment and preschool hours, absenteeism may be higher. This framework also highlights the importance of norms and values in parental decisions surrounding childcare. As discussed earlier, whether an individual parent values preschool in the same way society values later schooling is likely to have important implications for how likely they are to allow their child to miss extensive time from preschool. Supporting these theories, the K-12 literature suggests that the reasons underlying children’s school absences are complex and cut across different layers of the home and school systems in addition to the communities in which families reside (e.g., Baker et al., 2001; Epstein and Sheldon, 2002; Morrissey et al., 2014; Gottfried, 2015). Although a number of these features are likely to be important at the preschool level, it is important to consider the specific factors relevant to preschool absences, especially in the context of Head Start. In addition to serving children from lower-income families, Head Start has a longstanding focus on parent engagement which may shape absenteeism patterns in unique ways. Given that Head Start is the largest federal preschool program, understanding predictors of absenteeism in this context is critical. We ground the factors we examine in these theories, the Head Start context, and their policy relevance. Understanding how 104 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 03 frontiersin.org various factors shape absenteeism provides information that can be used to improve attendance in the future. We focus on both family- and classroom factors, as both are shaped by policy and practice. Although many of these factors have not been examined in the context of Head Start, we rely on research on absenteeism in elementary school to describe them below. Family circumstance/necessity Although children’s health has been found to predict higher levels of absenteeism during the elementary and secondary school years (Allensworth and Easton, 2007; Ready, 2010; Childs and Lofton, 2021), other mechanisms, similar to those described in the accommodations framework have also been considered. For example, because parents sometimes consider preschool to be childcare, and less of an educational opportunity, how frequently their child attends may be driven by how much they need childcare and how frequently their need for care overlaps with the hours the program is open. Thus, family factors such as the other adults in the home and maternal employment may be associated with absenteeism. Family stress and routines . Other family factors, including the levels of stress and chaos within the home may make it more difficult for children to consistently attend preschool, as these challenges may make it difficult for families to get their child to school. Alternatively, these families may have a greater need for out of home care and thus, limit the number of times their child is absent. Family poverty is a related factor that has been documented as a consistent predictor of absenteeism, in part due to the reasons discussed above, but also because it is associated with poor neighborhood conditions and community violence, which make it more difficult for families to get to school (Chen et al., 2000; Allensworth and Easton, 2007; Gottfried, 2010; Ansari and Gottfried, 2020). Children’s academic and social-behavioral skills In addition to the above family processes, another important dimension emphasized by bioecological theory includes the attributes and skills of children (Bronfenbrenner and Morris, 2006). These skills and behaviors can either support or impede children’s experiences, including their school attendance. For example, children who demonstrate low skills or problematic behaviors may signal to their parents that they need more help, and thus, reduce the likelihood of absenteeism (i.e., compensatory effects). On the other hand, children who demonstrate more optimal skills and behaviors may encourage parents to continue to invest in their education and, consequently, parents may take extra efforts to make sure their children are not absent from school (i.e., enrichment effects). Although both possibilities have received theoretical support, the evidence in the K-12 literature with respect to absenteeism has been mixed (e.g., Gottfried and Gee, 2017; Ansari et al., 2020). Center and classroom processes Despite the challenges faced by low-income families, the center itself also has the potential to influence the frequency of absenteeism. For example, centers that make an effort to meet with families or provide transportation and medical care may increase the ability of families to attend preschool regularly (e.g., Gottfried, 2013, 2017). Similarly, centers that make efforts to increase parents’ beliefs in the importance of preschool for their children’s future are also likely to reduce absenteeism (Ehrlich et al., 2013). Children’s relationships with their teacher are also important to their overall schooling experience (Crosnoe et al., 2004). If a child has a close relationship with their teacher, and more generally, positive experiences within the classroom, they may be more likely to want to attend preschool. Likewise, if parents perceive the classroom as being a positive experience for their child, they may do more to ensure that their child is present as much as possible. These potential influences may be particularly relevant in preschool when parental beliefs are so variable. Despite the clear rationale for hypothesizing that these cross- contextual factors would shape children’s preschool absences, most have not been examined empirically. To push the early childhood field forward we need to test these hypotheses, which requires theoretically grounded and advanced research methods. Thus, we sought to fill these gaps by examining the reasons underlying children’s preschool absences in a national sample of Head Start attendees. Because prior research has shown that one additional absence is not as detrimental for children’s academic achievement, but rather it is the accumulation of multiple days missed, we examine predictors of chronic absenteeism in addition to overall absences. Similar to other studies, we define chronic absenteeism as missing at least 10% of the school year (Balfanz and Byrnes, 2012). Taken together, this study can identify factors that can be targeted in the future to increase children’s preschool attendance, and ultimately, increase their kindergarten readiness. Materials and methods FACES 2009 followed a nationally representative sample of 3,349 3- and 4-year-old first time Head Start attendees across 486 classrooms (Moiduddin et al., 2012). For the purposes this investigation, we used data from the Head Start year (fall 2009 and spring 2010) and we excluded 444 children who did not have a valid longitudinal weight and 63 children who were in a home- based program, resulting in a final analytic sample of 2,842 children and families. On average, our final sample of children (50% female) were 3.84 years of age with the majority coming 105 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 04 frontiersin.org from ethnic minority households (36% Latine, 34% Black, 8% Asian/other). Table 1 presents full sample descriptives. Missing data ranged from 0–17%, with an average of approximately 6% per indicator. In total, there were roughly 200 patterns of missing data. Approximately, 60% of children had complete case data. The most common pattern of missingness involved missing data on indicators of social support, employment, and absenteeism (7%). The next most common patterns involved missing data on classroom quality (5% of cases), maternal education (3% of cases), academic assessments (3% of cases), absenteeism (3% of cases), and maternal employment and TABLE 1 Weighted descriptive statistics for focal variables. Variables M SD Absenteeism Proportion of days child was absent 0.05 0.04 Child was chronically absent Family necessity 0.12 Number of adults in the household 1.99 0.95 Number of children in the household 2.60 1.23 Parents marital status Married 0.29 Single 0.18 Not two parent household 0.53 Mothers’ employment status Full time 0.27 Part time 0.21 Unemployed 0.52 Mother enrolled in classes 0.25 Ratio of income to poverty 2.52 1.36 Other child care No other care 0.65 Relative care in home 0.12 Relative care out of home 0.14 Center-based care 0.10 Social support 2.52 0.50 Sources of social supporta Child’s father is helpful 0.64 Child’s father is not helpful 0.27 Spouse is helpful 0.40 Spouse is not helpful 0.06 Child’s grandparents are helpful 0.73 Child’s grandparents are not helpful 0.16 Relatives are helpful 0.78 Relatives are not helpful 0.19 Friends are helpful 0.71 Friends are not helpful 0.25 Head Start is helpful 0.84 Head Start is not helpful 0.14 Other Head Start parents are helpful 0.39 Other Head Start parents are not helpful 0.46 Stress and routines Food insecurity 0.39 0.59 Adequacy of medical care 0.94 0.13 Residential instability 0.49 0.83 Receipt of government benefits 0.30 0.20 Receipt of child support 0.22 0.41 Number of days family eats dinner together 5.36 1.76 Mothers’ depressive symptoms 4.89 5.82 (Continued) TABLE 1 (Continued) Variables M SD Mom has poor health 0.17 Child has poor health 0.05 Child’s hours of sleep 10.39 0.89 Child has regular sleep schedule 0.89 Mothers’ perception of neighborhood violence 0.73 1.23 Children’s early skills Behavior problems −0.06 0.96 Social skills 0.01 0.98 Academics 0.08 0.75 Center and classroom processes Frequency of home visits 2.17 1.48 Frequency of parent-teacher meetings 2.68 0.97 Services provided to families 0.76 0.15 Quality of teacher-child interactions (CLASS) 4.07 0.49 Child enjoys school 3.83 0.42 Parent feels welcome at school 3.78 0.48 Number of children chronically absent 1.66 0.65 Classroom behavior is good 3.39 0.81 Covariates Child race/ethnicity White 0.21 Black 0.34 Latine 0.36 Asian/other 0.08 Child gender (male)0.50 Child has a disability 0.06 Mother born in the U.S.0.71 Program is full day 0.60 Child 1 year away from kindergarten 0.43 Child age (months)46.09 6.65 Mothers’ age (years)28.83 5.89 Household language not English 0.24 Mothers’ education 1.99 0.92 aProportions for social support will not sum to 1.00 because an additional dummy variable was included for families who reported “not applicable.” 106 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 05 frontiersin.org coursework (3%). All other patterns of missing data represented less than 1–2% of cases. Measures Below, we describe our focal measures. Absenteeism During the spring parents were asked, “Approximately how many days has [CHILD] been absent since the beginning of the school year?” Responses were continuously measured and ranged from 0 to 20. Because not all parents reported on their children’s absences at the same time point (52% in March; 28% in April; and 20% in May), and because programs operated for a different number of days per week, we created an indicator of the proportion of days missed as a fraction of the days children were enrolled in school. To do so, we used parents date of assessment during the spring to gauge how long children were enrolled in school and divided the number of days children were absent by the number of months they were enrolled. This measured provided us with the number of days children were absent per month. Next, we multiplied the number of days children were absent per month by nine (the months of the school year). Finally, we divided this estimate by the number of days the program was in operation, which provided us with the proportion of the year children were absent. Chronic absenteeism was defined as missing 10% or more of the school year (Balfanz and Byrnes, 2012). Family circumstance/necessity We included nine parent-reported measures of family circumstances that may influence children’s absenteeism. We included two measures of household composition: The number of adults and number of children in the household. We have three categories to describe parental marital status: Married, not married, and not a two-parent household (i.e., cohabitating). Mothers’ employment status was coded as full-time, part-time, or not employed. We also included an indicator for whether the mother was currently enrolled in classes. The family financial situation was captured by the income-to-poverty measure (1 = less than 50% of the Federal Poverty Line; 6 = above 200% of the Federal Poverty Line). We also included measures of other sources of childcare that children experienced before or after Head Start, which included: Relative or non-relative care in home, relative or non-relative care not in home, center-based care, or no other care. Each of the aforementioned indicators was measured at the beginning of the Head Start year. Two aspects of social support were also included as part of families’ circumstances. These indicators were collected toward the end of the Head Start year. First, parents reported on six items that described how much social support they perceived having (α =0.86:1 = never true; 3 = always true). Sample items included “help watch child when parent runs errands” and “others will loan emergency cash.” Second, parents reported on how helpful they perceived the following sources to be in terms of helping with their children: child’s father, spouse, child’s grandparents, relatives, friends, Head Start, and other Head Start parents (1 = not very helpful; 2 = somewhat helpful; 3 = very helpful; 4 = not applicable). Due to the distribution of responses, we categorized responses into a dichotomous variable (0 = not very helpful, 1 = somewhat or very helpful) and included the not applicable response as a flag variable. Family stress and routines Parents also reported on several dimensions of family routines and stress, each of which was measured at the start of the Head Start year. First, family food insecurity was captured by a single item asking the frequency with which food runs out because of money (never true, sometimes true, often true). Adequacy of medical care was a sum of three items asking about whether the child had a doctor’s visit in the past year, a dental visit in the past year, and health insurance (Gershoff et al., 2007). Residential instability was the number of times the family moved in the past 12 months. Receipt of government benefits was the proportion of six benefits families received: TANF, unemployment insurance, Food Stamps, WIC, social security, and energy assistance. Mothers also reported on whether they received child support. Three items tapped into routines: The average number of hours the child slept, whether the child had at least 4 days a week that followed a regular sleep schedule, and the number of days per week the family ate dinner together. Two maternal health indicators were also included: mother’s depression, measured by 12 items from the CES-D (α = 0.86; Radloff, 1977), and whether the mother reported poor or fair health. An indicator for poor or fair child health was also included. Finally, mothers reported on their exposure to neighborhood violence using 4 items that captured whether parents saw violent or non-violent crimes in their neighborhood and whether they knew someone that was—or they themselves were—a victim of a violent crime. Responses were categorized into a 5-point scale capturing the severity of neighborhood violence (0 = witnessed no crimes; 5 = experienced a violent crime). Children’s early academic and social-behavioral skills Children’s early academic and social-behavioral skills were measured at the beginning of the Head Start year. First, children’s early academic skills were based on direct assessments of their language, literacy, and math skills. Language was captured by the Peabody Picture Vocabulary Test (Dunn and Dunn, 1997; α = 0.97), a measure of children’s receptive vocabulary. Literacy skills were captured through two subtests of the Woodcock-Johnson assessment, Letter- Word Identification (α = 0.85) and Spelling Word (α = 0.79; Woodcock et al., 2001). The two measures captured children’s ability to identify and write upper- or lower-case letters. Children’s math skills were also directly assessed with the 107 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 06 frontiersin.org Woodcock-Johnson Applied Problems subscale (α = 0.87; Woodcock et al., 2001). These measures were composited together to create an overall indicator of early academic achievement (α =0.74). Next, children’s behavior problems were reported on by teachers using 14 items from the Personal Maturity Scale (Entwisle et al., 1987) and the Behavior Problems Index (Peterson and Zill, 1986), which captured children’s aggressive hyperactive, and withdrawn behavior (α = 0.88). Finally, as part of the data collection, teachers also reported on children’s social skills (e.g., how often children followed directions, helped put things away, followed rules) using 12 items from the Personal Maturity Scale (Entwisle et al., 1987) and the Social Skills Rating System (Gresham and Elliott, 1990; α = 0.89). Center and classroom processes To capture center and classroom processes, we leveraged data from parents, teachers, and administrators. First, toward the end of the Head Start year, the child’s teacher reported on the frequency with which they performed home visits and their frequency of parent-teacher meetings. Next, at the beginning of the year, the center director reported on 15 different services provided to families (0 = no, 1 = yes), which were summed together (α = 0.72; e.g., medical care, dental care, transportation, and education or job training). All Head Start classrooms were also observed and rated on the CLASS in the spring (Pianta et al., 2008), which provides a measure of the quality of teacher- child interactions. The CLASS is based on a 7-point Likert scale (1–2 = low to 6–7 = high) and measures instructional, social– emotional, and organizational aspects of the classroom. Next, in the end of the year surveys, teachers reported on the number of children in their classroom who were chronically absent (1 = none, 4 = 5 or more) and the overall behavior of the classroom (1 = the group misbehaves very frequently and is almost always difficult to handle, 5 = the group behaves exceptionally well). Finally, in the end of year surveys, parents provided their perceptions of the center through seven items (1 = never, 4 = always), which were used to create two scales: parents’ feelings of welcomeness at the school (α = 0.74; e.g., teacher is supportive of parent, parent feels welcome by teacher) and children’s enjoyment of school (α = 0.64; e.g., child feels safe at school; child is happy at Head Start). Covariates In addition to the focal predictors discussed above, we also included a number of covariates that were collected at the start of the Head start year, namely: Child race/ethnicity, child gender, child disability status, mothers’ immigration status, whether the program was full day, whether the child was less than 1 year away from kindergarten, child and mother age, home language, and maternal education. Because of the large number of variables included, we examined all predictors for multi-collinearity issues and found none. Less than 1% of correlations among predictors were above 0.50. Analysis plan Two sets of analyses were estimated using (StataCorp, 2011). First, we estimated OLS models to examine the associations between the predictors and the continuous measure of absenteeism. For these models, we provide effect sizes that correspond with how many standard deviations (SDs) our dependent variables change per SD increase in our continuous predictors. Given the categorical nature of some of our predictors (where SDs are not meaningful), for those variables (e.g., employment), we provide effect sizes that correspond with the unstandardized regression coefficient divided by the SD of the dependent variable. Second, we estimated logistic regression models to examine the predictors of the dichotomous chronic absenteeism variable. To gauge the meaningfulness of these associations we provide odds ratios which capture the differences in chronic absenteeism given a one-unit change in the predictor. To facilitate interpretation across variables, we also also provide a percent change in rates of chronic absenteeism given a one standard deviation change in all continuous variables. All models were clustered at the classroom level to account for dependence in child outcomes and weighted to be nationally representative. To account for missing data, we imputed 50 datasets using the chained equations method. Additionally, because absenteeism is not distributed uniformly across schools and communities, we treat the above analytic framework as our primary specification and allow the variances in absenteeism to vary across different contexts. However, as an additional analysis, we also estimated additional models that implemented classroom fixed effects. In these models, we constrained the analysis to examining children within classrooms and, as such, we hold constant all classroom-level practices and processes. Thus, the classroom fixed effects models consider why some children are more (or less) likely to be absent than their classmates. Although not intended to be causal, classroom fixed effects provide a more rigorous estimation of how individual child and family factors are associated with absenteeism. We present both analytic specifications to provide a more balanced and nuanced portrait about absenteeism in Head Start. In doing so, it is important to note that classroom fixed effects models cannot be implemented with logistic regression and, consequently, when looking at chronic absenteeism as the outcome, we estimate a linear probability model. Coefficients for those models can be interpreted as the percentage change as a function of a one unit change in the predictor. Lastly, we estimated a robustness check using fractional response models for our OLS models due to the nature of our dependent variable. Results On average, children missed 5% of the school year and 12% were chronically absent. 108 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 07 frontiersin.org Predictors of absenteeism Our first model predicted the proportion of days a child was absent. Unstandardized and standardized coefficients are presented in the left two columns of Table 2. To begin, we found that very few family necessity and social support factors predicted children’s absences. However, the need for preschool (as captured by full-time employment), presence of siblings, and social support received, especially from other parents in the program, were linked with fewer school absences, with effect sizes ranging from roughly 5–15% of a SD. And even though children’s early academic and social- behavioral skills at the start of Head Start were not linked with absenteeism, family stress and routines did matter. More specifically, children whose families received greater government assistance were absent more often, whereas children who experienced more frequent family dinners had fewer absences. Not surprisingly, children in poor health missed a considerable more amount of school (ES = 33% of a SD) and so did children who lived in neighborhoods perceived by their mother to be violent. Moving beyond the home context, we also found that a number of center and classroom characteristics were linked with preschool absences. For example, children who enjoyed school were less frequently absent and so were children who attended classrooms that provided higher quality services. In contrast, there was evidence of spillover effects, whereby children were more frequently absent when they were enrolled in classrooms with a higher proportion of absent peers. Effect sizes for these associations ranged from approximately 5–10% of a SD. And although not a focal study objective, in terms of covariates, we found that Black and Latine children had fewer absences than White children. In contrast, children born to immigrant mothers had fewer absences than those whose mothers were born in the U.S. and children who attended a full-day program were also absent less frequently than children in part-day programs. Predictors of chronic absenteeism Our second model predicted whether children were chronically absent from Head Start. Unstandardized coefficients and odds ratios are provided in final two columns of Table 2. Overall, the patterns of results were similar to those documented above for absenteeism continuously measured, but there were a few notable differences. When looking at family necessity, the three significant associations were the same as above: A greater number of children in the household, maternal full-time employment, and support from other Head Start parents were all predictive of a lower likelihood of chronic absences. In terms of family routines and stress, we again found that receipt of governmental benefits was associated with increased preschool absences; however, unlike our models predicting overall levels of absences, when predicting chronic absences, we found that the adequacy of medical care was linked with a lower likelihood of chronic absenteeism. Poor child health was again a sizeable predictor of chronic absenteeism, but unlike our first model predicting overall levels of absences, children’s sleep patterns was associated with chronic absenteeism. Specifically, children who had more hours of sleep per night were less likely to be chronically absent. Like above, a similar pattern of center and classroom factors were also documented when examining how likely children were to be chronically absent, but many of the predictors were only marginally significant. And, in terms of covariates, the same patterns emerged. Black and Latine children (versus White children) were less likely to be chronically absent and children in full-day programs were less likely to be absent than those in half-day programs. Classroom fixed effects Our next set of analyses implemented classroom fixed effects (see Table 3). These results are presented in Table 3. Results from these analyses were generally similar to those reported above, but fewer stress and routine variables were associated with absenteeism when comparing children with their classmates. Importantly, however, even though fewer factors were significantly linked with within classroom absenteeism, the effect sizes for the focal associations were comparable across both specifications, suggesting that the reasons children were absent are comparable when making both within and between classroom comparisons. Fractional response models As a robustness check, we ran our OLS models using fractional response modeling. Because we had not included bounds for our imputations, 1–2% of cases had values that fell below 0%. To estimate fractional response models, we estimated models that: (a) excluded these 1–2% of cases and (b) recoded their values as 0. In both instances, our results were substantively similar to the results presented in Table 2 (results available from author upon request). Discussion Preschool absences are not uncommon. In fact, our results show that Head Start attendees miss approximately 5% of the school year on average and 12% of children are chronically absent. Because preschool attendance has been linked to improved academic achievement and later school attendance (Connolly and Olson, 2012; Ansari and Purtell, 2018; Ehrlich et al., 2018; Fuhs et al., 2018; Rhoad-Drogalis and Justice, 2018), it is critical to understand why children miss time from school. Resonating with both bioecological theory (Bronfenbrenner and Morris, 2006) and 109 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 08 frontiersin.org TABLE 2 Results from regression models predicting absenteeism and chronic absenteeism. Absenteeism Chronic absenteeism B (SE)β B (SE)OR % Diff. Family necessity Number of adults in the household −0.000 (0.001)−0.01 −0.014 (0.081)0.99 −1% Number of children in the household −0.004 (0.001)***−0.10 −0.218 (0.069)**0.80 −24% Parents marital status Single −0.000 (0.004)−0.01 0.216 (0.300)1.24 24% Not two parent household −0.005 (0.003)−0.12 −0.079 (0.242)0.92 −8% Mothers’ employment status Full time −0.006 (0.003)*−0.13 −0.496 (0.213)*0.61 −39% Part time −0.003 (0.003)−0.06 −0.242 (0.202)0.79 −21% Mother enrolled in classes −0.004 (0.002)−0.09 −0.262 (0.204)0.77 −23% Ratio of income to poverty 0.001 (0.001)0.03 0.031 (0.058)1.03 4% Other child care Relative care in home 0.003 (0.003)0.06 0.204 (0.268)1.23 23% Relative care out of home −0.001 (0.003)−0.02 −0.157 (0.251)0.85 −15% Center-based care −0.003 (0.004)−0.07 −0.172 (0.284)0.84 −16% Social support 0.005 (0.002)*0.06 0.262 (0.196)1.30 14% Sources of social supporta Child’s father is helpful −0.001 (0.003)−0.03 0.016 (0.192)1.02 2% Spouse is helpful 0.004 (0.004)0.09 0.083 (0.339)1.09 9% Child’s grandparents are helpful −0.005 (0.003)−0.12 −0.249 (0.230)0.78 −22% Relatives are helpful 0.002 (0.003)0.04 −0.186 (0.225)0.83 −17% Friends are helpful −0.001 (0.003)−0.03 0.137 (0.211)1.15 15% Head Start is helpful 0.000 (0.003)0.01 0.089 (0.250)1.09 9% Other Head Start parents are helpful −0.006 (0.002)**−0.13 −0.438 (0.172)*0.65 35% Stress and routines Food insecurity 0.001 (0.002)0.01 0.257 (0.133)+1.29 16% Adequacy of medical care −0.011 (0.008)−0.03 −1.229 (0.489)*0.29 −15% Residential instability −0.000 (0.001)−0.00 −0.016 (0.089)0.98 −2% Receipt of government benefits 0.015 (0.005)**0.07 1.019 (0.403)*2.77 22% Receipt of child support −0.001 (0.003)−0.03 −0.186 (0.203)0.83 −17% Number of days family eats dinner together −0.001 (0.001)*−0.05 −0.064 (0.043)0.94 −11% Mothers’ depressive symptoms 0.000 (0.000)+0.04 0.001 (0.013)1.00 6% Mom has poor health 0.002 (0.003)0.05 0.098 (0.214)1.10 10% Child has poor health 0.015 (0.005)**0.33 0.826 (0.267)**2.28 128% Child’s hours of sleep −0.001 (0.001)−0.03 −0.211 (0.098)*0.81 −17% Child has regular sleep schedule −0.003 (0.003)−0.06 −0.426 (0.227)+0.65 −35% Mothers’ perception of neighborhood violence 0.002 (0.001)**0.07 0.151 (0.062)*1.16 20% Children’s early skills Behavior problems −0.002 (0.001)−0.04 −0.034 (0.108)0.97 −3% Social skills −0.002 (0.001)−0.04 −0.050 (0.100)0.95 −6% Academics 0.001 (0.002)0.02 0.195 (0.152)1.22 14% Center and classroom processes Frequency of home visits −0.001 (0.001)−0.03 −0.032 (0.057)0.97 −4% Frequency of parent-teacher meetings −0.001 (0.001)−0.02 −0.028 (0.081)0.97 −3% Services provided to families −0.016 (0.008)+−0.05 −0.998 (0.512)+0.37 −14% Quality of teacher-child interactions (CLASS)−0.004 (0.002)*−0.05 −0.274 (0.163)+0.76 −13% Child enjoys school −0.009 (0.003)**−0.08 −0.354 (0.197)+0.70 −14% Parent feels welcome at school 0.002 (0.003)0.03 0.046 (0.184)1.05 2% Number of children chronically absent 0.006 (0.002)***0.09 0.342 (0.116)**1.41 25% (Continued) 110 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 09 frontiersin.org models of preschool selection (Meyers and Jordan, 2006), our results highlight the multifaceted nature of preschool absences, with multiple factors across contexts contributing to the likelihood that children would miss time from preschool. We found a number of family factors that were associated with children’s absences. To start, children who had mothers that were employed full-time and children who were in a household with a greater number of children were both less likely to be absent and chronically absent. Families with multiple children and full-time employment may rely on preschool for childcare, and thus, be less likely to let their child miss significant time from school. The finding on number of children in the home is different from qualitative findings with elementary-aged children, where having a greater number of children in the household can make getting to school more challenging (Sugrue et al., 2016). Children whose families had more frequent routines were also absent less frequently than children whose families had fewer daily routines. Specifically, more family dinners were associated with fewer absences, and both the regularity and amount of children’s sleep were also associated with fewer absences. That families that are more regular in their routines at home would also be more routine in their children’s preschool experience is perhaps not surprising as more routines in the home are likely to mitigate stressors associated with absenteeism. Similar to the existing literature on elementary school absences (e.g., Ready, 2010), we found that children’s health was strongly associated with absences and chronic absenteeism in preschool. On the contrary, neither mothers’ physical nor mental health played a role in their children’s absences, although it is plausible that these characteristics shaped absenteeism through their associations with family routines. Similar to prior research, a number of indicators of economic stressors were also associated with absenteeism and chronic absenteeism, including food insecurity, adequacy of medical care, and receipt of governmental assistance (Chang and Romero, 2008). These economic challenges are likely to be associated with day-to-day barriers to attendance, such as transportation, which is an important correlate of regular school attendance (Gottfried, 2017). Lastly, mothers who perceived their neighborhoods to be violent had children who were more frequently absent. It may be that this is operating as another marker of economic disadvantage, or it may be that living in dangerous neighborhoods poses a separate barrier to regular preschool attendance. Taken together, these findings suggest that social and economic disadvantages pose great challenges to high rates of attendance at Head Start. Programs focused on reducing absenteeism need to consider the complex circumstances families may be experiencing throughout the school year. More hearteningly, we found a number of center- and classroom-level features that were associated with fewer preschool absences. For example, children who attended centers that provided more services to families were less likely to be absent. This suggests that a continued focus on family outreach may benefit children by increasing attendance, in addition to its other positive impacts on families (e.g., Barnett et al., 2020). Children’s classroom experiences also played a role in the regularity of their attendance; specifically, children were less likely to be absent when their mothers’ perceived them as enjoying school and when they attended classrooms that were rated as higher quality. Thus, positive child experiences in the classroom is a potential pathway to reduced absenteeism. TABLE 2 (Continued) Absenteeism Chronic absenteeism B (SE)β B (SE)OR % Diff. Classroom behavior good 0.001 (0.001)0.02 0.151 (0.093)1.16 13% Covariates Child race/ethnicity Black −0.020 (0.003)***−0.45 −0.931 (0.240)***0.39 −61% Latine −0.009 (0.004)*−0.19 −0.649 (0.257)*0.52 −48% Asian/other −0.010 (0.005)*−0.23 −0.487 (0.339)0.61 −39% Child gender (male)0.002 (0.002)0.05 −0.002 (0.161)1.00 0% Child has disability −0.003 (0.005)−0.08 −0.094 (0.315)0.91 −9% Mother born in the United States 0.010 (0.004)*0.22 0.168 (0.300)1.18 18% Program is full day −0.009 (0.002)***−0.21 −0.698 (0.181)***0.50 −50% Child 1 year away from kindergarten 0.003 (0.004)0.08 0.042 (0.297)1.04 4% Child age −0.001 (0.00)+−0.07 −0.029 (0.022)0.97 −17% Mothers’ age −0.000 (0.00)−0.02 −0.024 (0.016)0.98 −14% Household language not English 0.001 (0.001)0.02 0.049 (0.341)1.05 5% Mothers’ education 0.000 (0.001)0.00 0.040 (0.093)1.04 4% aAlthough not shown, an additional dummy variable was included for the social-support variables representing those who reported not applicable. O.R. is odds ratios. The O.R. results are not using standardized predictors and thus can be interpreted as one unit increase on the original scale metric. To present a more comparable metric across predictors, the % diff column corresponds to the percent change in rates of chronic absenteeism given a one standard deviation change in continuous predictors. ***p < 0.001. **p < 0.01. *p < 0.05. +p < 0.10. 111 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 10 frontiersin.org TABLE 3 Results from regression models predicting absenteeism and chronic absenteeism using classroom fixed effects. Absenteeism Chronic absenteeism B (SE)β B (SE)βb Family necessity Number of adults in the household 0.001 (0.001)0.01 0.003 (0.008)0.00 Number of children in the household −0.003 (0.001)***−0.09 −0.018 (0.007)**−0.02 Parents marital status Single −0.000 (0.004)−0.00 0.015 (0.034)0.01 Not two parent household −0.003 (0.004)−0.07 0.005 (0.028)0.00 Mothers’ employment status Full time −0.006 (0.003)*−0.13 −0.048 (0.021)*−0.05 Part time −0.002 (0.003)−0.04 −0.021 (0.023)−0.02 Mother enrolled in classes −0.004 (0.003)−0.09 −0.037 (0.021)+−0.04 Ratio of income to poverty 0.001 (0.001)0.03 0.007 (0.006)0.01 Other child care Relative care in home 0.001 (0.003)0.02 0.008 (0.028)0.01 Relative care out of home 0.002 (0.003)0.04 0.011 (0.023)0.01 Center-based care −0.001 (0.005)−0.01 0.005 (0.033)0.01 Social support 0.004 (0.003)0.04 0.024 (0.021)0.01 Sources of social supporta Child’s father is helpful −0.002 (0.003)−0.06 −0.000 (0.021)−0.00 Spouse is helpful 0.006 (0.005)0.14 0.039 (0.035)0.04 Child’s grandparents are helpful −0.005 (0.003)−0.12 −0.021 (0.029)−0.02 Relatives are helpful 0.002 (0.003)0.04 −0.024 (0.027)−0.02 Friends are helpful −0.001 (0.003)−0.03 0.020 (0.025)0.02 Head Start is helpful 0.000 (0.003)0.01 0.006 (0.026)0.01 Other Head Start parents are helpful −0.006 (0.002)**−0.14 −0.046 (0.019)*−0.05 Stress and routines Food insecurity 0.001 (0.002)0.01 0.031 (0.016)+0.02 Adequacy of medical care −0.017 (0.008)*−0.05 −0.177 (0.067)**−0.02 Residential instability 0.000 (0.001)0.01 0.003 (0.011)0.00 Receipt of government benefits 0.015 (0.006)**0.07 0.113 (0.048)*0.02 Receipt of child support −0.000 (0.003)−0.01 −0.012 (0.022)−0.01 Number of days family eats dinner together −0.001 (0.001)*−0.06 −0.007 (0.005)−0.01 Mothers’ depressive symptoms 0.000 (0.000)+0.05 0.001 (0.002)0.01 Mom has poor health 0.003 (0.003)0.08 0.019 (0.026)0.02 Child has poor health 0.010 (0.006)+0.24 0.061 (0.038)0.06 Child’s hours of sleep −0.002 (0.001)−0.03 −0.019 (0.011)+−0.02 Child has regular sleep schedule −0.004 (0.004)−0.10 −0.068 (0.028)*−0.07 Mothers’ perception of neighborhood violence 0.002 (0.001)*0.06 0.013 (0.008)+0.02 Center and classroom processes Frequency of home visits −−−− Frequency of parent-teacher meetings −−−− Services provided to families −−−− Quality of teacher-child interactions (CLASS)−−−− Child enjoys school −0.008 (0.004)*−0.07 −0.043 (0.027)−0.02 Parent feels welcome at school 0.002 (0.003)0.02 0.001 (0.023)0.00 Number of children chronically absent −−−− Classroom behavior good −−−− Children’s early skills Behavior problems −0.001 (0.002)−0.03 −0.007 (0.014)−0.01 Social skills −0.002 (0.002)−0.04 −0.010 (0.014)−0.01 (Continued) 112 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 11 frontiersin.org Interestingly, the other social support item that was associated with fewer school absences was parents’ perceptions of support from other Head Start parents, suggesting that facilitating relationships between parents is another important way that centers may be able to reduce absenteeism. Despite these promising avenues for reducing preschool absences, we also found that the concentration of absences within a classroom was associated with individual children’s absenteeism. Although speculative, it may be that a high concentration of absences in a classroom reflects a social norm, namely that preschool absences are okay (see also, Ehrlich et al., 2013; Gottfried et al., 2020). Overall, our findings highlight the fact that no one mechanism stood out as the sole driver of absenteeism; but rather, there appeared to be many individual, family and center characteristics that shaped preschool absenteeism. This aligns with bioecological theory that suggests that multiple systems may shape absenteeism. Additionally, our findings provide support for multiple components of the accommodation’s framework. For example, children whose families likely had higher need for childcare, as evidenced by full-time employment, were less likely to be absent. But other factors mattered too, providing evidence for the framework’s assertion that parents’ decision-making around childcare, and in this case, attendance, is complex, and shaped by numerous factors. Accordingly, there are many routes to reduce absenteeism in the future—and focusing on one factor alone is unlikely to make drastic reductions in absenteeism. A holistic approach that tackles both family- and classroom-level processes is necessary to improve children’s Head Start attendance. Having said that, there are successful models at other school levels that may be useful to future program development. One such successful elementary school model assigned monitors to engage with both families and school staff to increase attendance; this type of model may be particularly useful in Head Start, which already strives to increase parent-center communication, but has not yet been tested in the preschool years (Lehr et al., 2004). Other work has revealed a number of promising strategies to reduce preschool absences. First, in line with our findings, Katz et al. (2016) note that home-school connections are critical to facilitate school attendance. Even so, it is important to acknowledge that these positive relationships may not be enough to reduce the barriers present for some families. Thus, having other resources, such as information about transportation and medical care referrals, easily accessible to families is critical to reducing preschool absences. Additionally, Katz et al. (2016) find that staff members commonly feel that parents do not understand the importance of preschool for their children’s current and future learning. Finding successful ways to deliver this message to families requires continued attention, as parents’ beliefs about preschool are likely key to reducing absenteeism. Despite the fact that our study represents one of the first efforts to understand why children miss time from preschool at the national level, our findings need to be interpreted in light of a few limitations. The primary limitation of our work is our reliance on parental report of children’s absences. Although the use of parent reports is common, administrative data that tracks children’s absences could increase precision when examining the predictors and outcomes of preschool absences. Nonetheless, FACES 2009 is one of only two national datasets with information on children’s preschool attendance (Mendez et al., 2016). Additionally, our data is limited to children attending Head Start and, thus, we cannot speak to the predictors of absenteeism in other types of preschool programs, which requires continued attention. Given that Head TABLE 3 (Continued) Absenteeism Chronic absenteeism B (SE)β B (SE)βb Academics 0.001 (0.002)0.01 0.012 (0.017)0.01 Covariates Child race/ethnicity Black −0.011 (0.005)*−0.25 −0.044 (0.036)−0.04 Latine −0.003 (0.005)−0.07 −0.033 (0.042)−0.03 Asian/other −0.005 (0.005)−0.12 −0.028 (0.045)−0.03 Child gender (male)0.002 (0.002)0.04 −0.003 (0.018)−0.00 Child has disability −0.002 (0.005)−0.04 0.004 (0.038)0.00 Mother born in the United States 0.008 (0.005)+0.18 0.011 (0.033)0.01 Program is full day −−−− Child 1 year away from kindergarten 0.002 (0.005)0.05 −0.000 (0.038)−0.00 Child age −0.000 (0.000)−0.03 −0.002 (0.003)−0.01 Mothers’ age −0.000 (0.000)−0.02 −0.002 (0.002)−0.01 Household language not English 0.005 (0.006)0.10 0.020 (0.040)0.02 Mothers’ education 0.001 (0.001)0.02 0.004 (0.010)0.00 aAlthough not shown, an additional dummy variable was included for the social-support variables representing those who reported not applicable. bTo generate standardized estimates for chronic absenteeism, only continuous variables were standardized to have a mean of 0 and standard deviation of 1. Thus, coefficients can be interpreted as the percentage difference between categories or the percentage change as a function of a one standard deviation change in the predictor. ***p < 0.001. **p < 0.01. *p < 0.05. +p < 0.10. 113 Purtell and Ansari 10.3389/feduc.2022.1031379 Frontiers in Education 12 frontiersin.org Start serves children from low-income families, our findings may be more generalizable to this population. However, given Head Start’s longstanding commitment to family and community engagement, our findings likely do not generalize beyond the program. Given our large number of predictors, it is also important to note the potential for the Table 2 fallacy in our interpretation of our findings (Westreich and Greenland, 2013). Although the goal of this paper was to identify unique associations with children’s absenteeism, it is plausible that some of our predictors (e.g., indicators of financial instability) are mechanisms through which other predictors (e.g., employment status) are associated with children’s absenteeism. Understanding these pathways is an important direction for future research. It is also important to note that although we examined numerous predictors, there are still a number of potential factors not addressed that are key for future research. For example, more direct measures of transportation and logistical support are important to capture (Gottfried, 2017). Additionally, understanding parents’ perspectives regarding the importance of attendance in the preschool years may be key to understanding absenteeism patterns (Ehrlich et al., 2013). Lastly, understanding predictors of absenteeism within demographic groups may be critical to developing potent interventions. For example, although we found that Black and Latine children were less likely to be absent than their White peers, understanding factors that shape absenteeism within these groups may be necessary to improve attendance in the future. Absenteeism, and particularly, chronic absenteeism is diminishing the potential benefits of preschool (Connolly and Olson, 2012; Ansari and Purtell, 2018; Ehrlich et al., 2018; Rhoad-Drogalis and Justice, 2018), especially for children who are from low-income families who: (a) are more likely to benefit from preschool (Weiland and Yoshikawa, 2013), but (b) are more likely to be absent than their higher-income peers (Morrissey et al., 2014). In this study, we find that there is no one underlying reason for absenteeism; rather, there are a number of factors that cut across contexts are contributing to the high levels of absences in the United States among a sample of preschoolers from low-income homes. As such, there is possible value of a package of efforts that target the different causes of absenteeism. Addressing these factors, and subsequently reducing absenteeism, is a critical pathway to increasing the school readiness of disadvantaged children. Data availability statement Publicly available datasets were analyzed in this study. This data can be found at: https://www.icpsr.umich.edu/web/ICPSR/ series/236. Ethics statement The studies involving human participants were reviewed and approved by the Ohio State University IRB. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Author contributions AA led analysis and preparation of methods and results. KP led preparation of introduction and discussion. AA and KP authors conceptualized the manuscript. All authors contributed to the article and approved the submitted version. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Allensworth, E. M., and Easton, J. Q. (2007). What matters for staying on track and graduating in Chicago public high schools. Chicago, IL: Consortium on Chicago School Research. Ansari, A., and Gottfried, M. A. (2020). Early childhood educational experiences and preschool absenteeism. Elem. Sch. J. 121, 34–51. doi: 10.1086/709832 Ansari, A., Hofkens, T. L., and Pianta, R. C. (2020). 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Educ. 20, 67–70. doi: 10.1177/027112140002000201 115 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 1 Community Services Bureau Monitoring Report Summary February 2024 – June 2024 Description: Community Services Bureau (CSB) implements ongoing monitoring of its operations and services. This process includes: (1) using measures, tools, or procedures to implement the system of ongoing monitoring; (2) assigning staff and/or consultants to the ongoing monitoring of each service; (3) collecting, analyzing, and reporting on the program’s progress towards its own goals for quality; and (4) following-up on and correcting any weaknesses identified through ongoing monitoring. This is conducted through a multi-level monitoring system of (1) Center Level; (2) Content Area Level; and (3) Agency Level. This summary report includes compiled results of the monitoring conducted for the period of February 2024-June 2024. Summary of Monitoring Activities: Monitoring was conducted for directly operated CSB centers, partner agencies, and the Delegate Agency, YMCA of the East Bay. This report highlights the data trends identified using monitoring tools focused on Classroom and Facility Environment, Education, and Comprehensive Services. During the program year 2023-2024, Community Services Bureau has maintained focus on the health and safety of the children, families, and staff served. This is vital to ensuring quality learning, as you must be healthy and secure in your environment to learn. During the second period of monitoring, the staff continued to actively engage in data review. Their focus remained on strengthening relationships, ensuring children are in healthy and safe environments, and enhancing the connection between home and school. Data sources utilized by the team during this monitoring period include classroom and facility observations and CLOUDS database reports. Top Trends: Analysis of the data across all tools and monitoring levels shows the following: • Continued Positive interactions between teachers and children, exceeding required guidelines. 116 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 2 • Professional development initiatives focused on health, safety, and administrative changes are visible in the overall monitoring data. • Preventable non-compliances continued to decrease from the previous monitoring period. The table below provides an overview of each monitoring tool included in the Semi-Annual Report. Monitoring Level Monitoring Tool Overall Compliance Period 1- 2023-2024 Overall Compliance Period 2- 2023-2024 Center Level Monitoring Tools Daily Classroom Health & Safety Checklists 99% 99% Daily Playground Safety Checklists 99% 99% *Weekly Facility Safety Checklists 98% 98% Monthly Playground Checklists 99% 99% Content Level Tools Child Safety & Transition Monitoring 97% 96% CACFP Center Monitoring Review 96% 92% Health & Safety Checklists 99% 99% Onsite Content Area Monitoring Tool 95% 98% CEU Eligibility Monitoring Tool 95% 94% Content Area File Monitoring- HS Eligibility 97% 97% Content Area File Compliance (Education & Comp Services) N/A 85% Agency Level Tools Center Monitoring 95% N/A Needs & Eligibility Monitoring Tool N/A 96% Education & Comprehensive Service File Review N/A 81% 117 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 3 Classroom Assessment Scoring System (CLASS) +Above Average +Below Average Curriculum Fidelity N/A +100% *Weekly Facility Checklists were previously Daily Facility Checklists +CLASS and Curriculum Fidelity observations are not measured by compliance vs. non-compliance, rather scaled by effectiveness. N/A indicates not conducted during the monitoring period. 118 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 4 WEEKLY FACILITY SAFETY CHECKLIST About The Tool: The Weekly Facility Safety Checklist is an observation tool to ensure the overall childcare facility space meets the safety component daily. The tool is completed daily by the Site Supervisor/Manager (or designee) by inputting information directly into the database management system CLOUDS. The Weekly Facility Safety Checklist has eight (8) items to review. The Site Supervisor or designee will mark non-compliance on an item until issue(s) are resolved. Successful Outcomes: Overall compliance was at 98% for Period 2. Since the tool implementation Site Supervisors have been more proactive in completing this tool. Ninety percent of the indicators do not require a corrective action plan Areas Needing Improvement Consistent use of the tool including weekly entry into CLOUDS (01): Outdoor environment is free of litter and unsafe debris (4.98%) (03): Weeds and branches are in different areas on the playground. (3.711%) (07) The refrigerator temperature has dropped the required temperature that can be unsafe for food storage. (3.711%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. DAILY PLAYGROUND CHECKLIST About The Tool: The Daily Playground Safety Checklist is an observation tool to ensure the playgrounds meet the safety component daily. The tool is completed on daily basis and prior to the arrival of children by the opening or designated teaching staff by directly inputting into the database management system CLOUDS. The Daily Playground Safety Checklist has five (5) items to review. Successful Outcomes: Overall compliance is 99% for Period 2. No corrective action plan is required. Teachers are ensuring that the outdoor environment and playground is safe for the children to use. Areas Needing Improvement Timeline for correcting non-compliances in CLOUDS Boundaries: Fencing or other barrier zone play areas are locked, secure, and in good repair, including gate latches and alarms. (1.345%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. DAILY CLASSROOM HEALTH & SAFETY CHECKLIST About The Tool: The Daily Classroom Health & Safety Checklist is an observation tool to ensure the classrooms meet the safety component daily. The tool is completed daily and prior to the arrival of children by the opening or designated teaching staff by inputting into the database management system CLOUDS. The Daily Classroom Health & Safety Checklist has thirteen (13) items to review. 119 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 5 Successful Outcomes: Overall compliance is 99% for Period 2. All non-compliances occur at a rate of less than 1%. Areas Needing Improvement Consistently making sure that wall pad/tablets are in working order at all times. (0.862%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. MONTHLY PLAYGROUND CHECKLIST About The Tool: The Monthly Playground Safety Checklist is an observation tool to ensure the playground/outdoor environment meet the safety component. The tool is completed monthly by the Site Supervisor/manager (or designee) by inputting into the database management system CLOUDS. The tool has sixteen (16) items to review. Successful Outcomes: Overall compliance is 99%. No corrective action plan is required for Period 2. Areas Needing Improvement Timeline for addressing concerns regarding rust on playgrounds. (1.67%) Timeline for addressing concerns regarding gates: latched and locked appropriately. (1.67%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. 120 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 6 CHILD SAFETY & TRANSITION MONITORING TOOL About The Tool: The CSB Child Transition & Safety Monitoring tool is an observation tool to ensure that key health and safety practices are implemented by program staff. This tool was designed to be a multi- level monitoring tool and is used by the Management Team and Site Supervisors. The tool contains fifteen (15) items to review. Successful Outcomes: Overall Compliance is 96% for period 2. Positive Teacher/Child interactions in compliance with Child’s Personal Rights and Standards of Conduct. Classroom teachers/staff can articulate methods of “active supervision” (i.e. scanning areas, counting using child name-to-face recognition, zoning maps, etc.) Areas Needing Improvement (3) Safe Environments are evident and promote active supervision all children, including door alarms. (11.11%) (10) The site/center parent communication board has current, relevant, and translated material posted. (11.11%) (13) CLOUDS review: A review in CLOUDS if the most recent site Weekly Facility Tool, Daily Classroom Health & Safety Checklist, and Daily Playground Checklist accurately reflect the findings in your observation today? (11.11%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. CHILD & ADULT CARE FOOD PROGRAM (CACFP) About The Tool: CSB Content Area Manager – Nutrition Manager or Designee conducts the CACFP Center Monitoring Review of CSB directly operated centers three times a year during the program year using the Center Monitoring Review Report Tool. The tool contains twenty-six (26) items to review. Successful Outcomes: Overall compliance: 92% 18 of 26 items were 100% compliant. Areas Needing Improvement: 3 top non-compliance are the following: o 22a. Were there problems noted in the prior site review? o 23a. Does this visit indicate that training is necessary at this facility? o 23b. If training is needed, state when and how it will be provided? Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. HEALTH & SAFETY CHECKLIST-Quarterly About The Tool: The Health & Safety Checklist is an observation tool to ensure the facility is free of hazards. The tool is completed quarterly by the Health Safety Officers (or designee) and Partners by inputting into the database management system CLOUDS. The tool has forty-nine (49) indicators to review for directly operated and forty-four (44) indicators to review for Partners. 121 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 7 Successful Outcomes: Overall compliance is 99% for period 2 45 of 49 items had zero non-compliances. Areas Needing Improvement: (05) The temperature in the refrigerator set below 40°F and the freezer below 0°F. (1.33%) (33) Tall furniture over 4 feet is secured to the wall or floor. (1.33%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. ON-SITE CONTENT AREA MONITORING TOOL About The Tool: The CSB On-Site Content Area Monitoring tool is an observation tool to ensure that program staff implement key comprehensive service practices in the classroom. The tool is completed twice a program year by the Comprehensive Services Management Team (or designee) by inputting information directly into the database management system CLOUDS. The tool contains thirty-six (36) items to review. Successful Outcomes: Overall compliance is 98%. 22of 36 items were 100% compliant. Areas Needing Improvement: (31) All medications are current and stored in original packaging with dose instruction from the medical provider. (17.21%) (15) The outdoor play area is free of hazards and arranged to allow children using adaptive devices to safely participate in play with peers. (10.34%) Timeline for correcting non-compliances in CLOUDS Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. CENTRALIZED ENROLLMENT UNIT (CEU) ELIGIBILITY MONITORING TOOL About The Tool: The Centralized Enrollment Unit (CEU) Eligibility File Monitoring tool is an observation tool to ensure child meets eligibility and need for Federal and State guidelines. The tool is completed by CEU's Comprehensive Services Assistant Manager or designee. Four (4) files are reviewed monthly, and information is saved directly into the database management system CLOUDS. The tool contains eight (8) indicators to review. Successful Outcomes: Overall compliance is 94%. All children met eligibility criteria established by funding source(s) 2 Centers that were monitored were 100% compliant. Areas Needing Improvement: (8) CD9600 Section IV Contracted children(s)’s gender, adjustment factor code, ethnicity, race, language, program code, type of care, and provider code are noted. (14.74%) (5) All areas of income worksheet are completed, correct and signed. (13.15%) (7) Notice of Action is complete, current, and matches 9600/9600S and Admission Agreement. (10.52%) 122 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 8 Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. Content Area File Monitoring- HS Eligibility About The Tool: The Centralized Enrollment Unit (CEU) Head Start (HS) Eligibility File Monitoring tool is an observation tool to ensure child meets eligibility and need for Federal and State guidelines. The tool is completed by CEU's CEU Eligibility Analyst or designee. Four (4) files are reviewed monthly, and information is saved directly into the database management system CLOUDS. The tool contains five (5) indicators to review. Successful Outcomes: Overall compliance is 97%. 3 out of the 5 items reviewed were compliant. Areas Needing Improvement: (5) All areas of income worksheet are completed, correct and signed. (12.65%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. CONTENT AREA FILE COMPLIANCE (EDUCATION & COMPREHENSIVE SERVICE) About The Tool: The Content Area File Compliance is a file review to ensure that the content areas that are related Health (Oral and Physical), Nutrition, Mental Health, Disabilities, Family Engagement, and Education are compliant in meeting state and federal guidelines. This is led by the Content Area Managers and Comprehensive Service Assistant Managers and Senior Management. The file review will be conducted on 100% of the files for Directly Operated, Partners and Delegate. This tool consists of nineteen (19) items to review. Successful Outcomes: Overall compliance is 85%. (13) If applicable: a Positive Guidance Policy Step Letter to Parents (such as CSB521), and Positive Guidance Plan (such as CSB134B) are followed and in file. Areas Needing Improvement: (7) (F) Dental exam is current, received withing 90 days of enrollment and annually thereafter. If applicable, entered in electronic record systems. (37.35%) (10) (F) Family Partnership Agreement completed within sixty (60) day of enrollment and follow up evident at the end of the year. Entered un electronic record system as appropriate and completed annually thereafter. (33.90%) (5) (L) Prior to, or within thirty (30) calendar day of child’s enrollment, a completed health exam is obtained and if applicable entered in electronic record systems. (29.88%) Corrective Actions: Corrective Action Plan and Root Cause Analysis is required and will be validated. 123 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 9 NEED & ELIGIBILITY About The Tool: Quality Management Unit (QMU) reviews a random selection of files of enrolled children, in the current program year, five (5) files per funding model for each center using the Need & Eligibility Compliance Monitoring Tool. The tool contains twenty-one (21) items to review in each file. Successful Outcomes: Overall compliance is 96.1% 7 out the 19 items were 100% compliant Areas Needing Improvement: Highest non-compliance items: (6) CD-9600: (S) All fields in heading information and section I are completed and checked. (15.942%) and (9) CD-9600: (S) Section IV Contracted child(ren)’s gender, adjustment factor code, ethnicity, race, language, program code, type of care, and provider code are noted. (13.043%) Corrective Actions: Corrective actions taken at site level and no corrective action plan is required. EDUCATION AND COMPREHENSIVE SERVICES FILE REVIEW About The Tool: The Quality Management Unit reviews thirty percent (30%) of enrolled children, in the current program year, per classroom for each center using the Education and Comprehensive Services Compliance Monitoring Tool. The tool consists of areas related to Health (Oral and Physical), Nutrition, Mental Health, Disabilities, Family Engagement and Education. There are forty (40) items to review. Successful Outcomes: Overall Compliance is 81% 2 out of the 39 items reviews were compliant Areas Needing Improvement: (9) Subsequent Health exams are obtained and current to follow the EPSDT schedule and entered in electronic record systems) as appropriate. (79.86%) (34) (S)(F) Ongoing education assessments (such as DRDP) are conducted within 60, 180, and 300 days of enrollment. Federal programs require three assessments per year. (77.78%) (11) (F) Dental exam is current, received within 90 days of enrollment and every six (6) months thereafter. If applicable entered in electronic record systems. (58.33%) Corrective Actions: Corrective Action Plan and Root Cause Analysis is required and will be validated. Curriculum Fidelity About The Tool: The Quality Management Unit reviews thirty percent (30%) classrooms. To ensure that the curriculum used to it is fidelity. Successful Outcomes: Overall Compliance is 100% Five centers that were observed were 100% compliant. Areas Needing Improvement: None at this time. 124 Contra Costa County Employment & Human Services Department – Community Services Bureau 2023-2024 Ongoing Monitoring Report Semi-Annual Summary Report (February-June)-Final August 2024 Page | 10 Corrective Actions: No corrective action plan is required. CLASSROOM ASSESSMENT SCORING SYSTEM (CLASS) About The Tool: The Quality Management Unit (QMU) conducts CLASS monitoring to a sample of preschool classrooms using the CLASS Tool. The Classroom Assessment Scoring System (CLASS®) is an observation instrument that assesses the quality of teacher-child interactions in center-based preschool classrooms. CLASS® includes three domains or categories of teacher-child interactions that support children's learning and development: Emotional Support, Classroom Organization, and Instructional Support. CLASS® is scored by trained and certified observers using a specific protocol. Following their observations of teacher-child interactions, CLASS® observers rate each dimension on a 7-point scale, from low to high. • Scores of 1-2 mean the quality of teacher-child interactions is low. Classrooms in which there is poor management of behavior, teaching that is purely rote, or that lack interaction between teachers and children would receive low scores. • Scores of 3-5, the mid-range, are given when classrooms show a mix of effective interactions with periods when interactions are not effective or are absent. • Scores of 6-7 mean that effective teacher-child interactions are consistently observed throughout the observation period. Countywide Total Average Domain Score 2023-2024 CSB Threshold Federal Quality Threshold Federal Competitive Threshold Domain Score Score Score Score Emotional Support 6.03 6 6 5 Classroom Organization 5.15 6 6 5 Instructional Support 2.92 3 3 2.30 Corrective Actions: Corrective actions plan is required. 125