In this Q&A originally published by The Duke Endowment, Meghan McCormick describes MDRC’s ongoing evaluation of the promising Child First home visiting model — and talks about finding a silver lining in confronting the COVID-19 pandemic.
Even before the COVID-19 crisis, early care and education providers faced challenges attracting and retaining qualified, well-trained, and diverse early educators — and staff turnover can affect children’s early progress. Three approaches may help improve these workers’ access to professional education, their overall economic well-being, and their sometimes difficult working conditions.
Children in low-income communities are less likely than others to attend programs that improve kindergarten readiness. MDRC has identified two ways to promote more equitable access: Make information about existing high-quality programs easier to understand and improve quality by investing in curricula and professional development.
Semistructured interviews involve an interviewer asking some prespecified, open-ended questions, with follow-up questions based on what the interviewee has to say. This Reflections on Methodology post describes a semistructured interview protocol recently used to explore how children who experience poverty perceive their situations, their economic status, and public benefit programs.
Expanded eligibility guidelines and flexible funding options can support wider access to child care during the COVID-19 emergency, but only if parents and child care workers know how to navigate them. Agencies can use behavioral science research insights to make communications clear and concise and simplify the application process.
Home Visiting and Coordinated and Integrated Early Childhood Systems
Funders at all levels are investing in programs to support expectant parents and families with young children. MDRC is conducting research in that field in three areas: integrating systems of services that work together, getting families and children the right services, and building evidence about promising models.