How Well Are Early Care and Education Providers Who Serve Hispanic Children Doing on Access and Availability?
Bibliographic record
Abstract
OverviewEarly care and education (ECE) programs serve an important developmental support for children, helping to reduce gaps in school readiness and in later educational outcomes, particularly for low-income children.1,2,3 ECE programs-and child care subsidies in particular-also represent an important employment support for parents.Given their role in supporting parents' employment and reducing gaps in school readiness, public investment in recent decades has focused on increasing access to and the quality of ECE programs.After decades of lagging behind, Latino a children-including those who are low-income-are enrolling in ECE programs at rates approaching those of their low-income white peers, at least among preschool-aged children.4,5 However, we still know little about the providers of ECE programs (both formal and informal) that care for and serve Latino children.Given the increasing enrollment of Hispanic children in ECE programs, what do the programs that serve this population look like?This brief provides a national portrait of providers serving a large proportion b of Hispanic children, focusing on characteristics that shape access to and availability of ECE programs.We find that roughly one in five providers serve a high proportion of Hispanic children (also referred to as high-Hispanic-serving), in which 25 percent or more of the children enrolled are Hispanic.Collectively, our findings suggest many ways in which providers-and home-based providers in particular-are likely responding to the needs of Hispanic families, as well as possible areas of unmet need. About the StudyParents consider a variety of factors when selecting an ECE program.We selected provider or program characteristics likely to influence access to and availability of care.These include such factors as the number and timing of hours of care offered and flexibility in payment for (and hours of ) care.Additionally, we examined the extent to which providers have refused to care for a child either because of a lack of space or due to children's behavioral issues.We compared these indicators of access and availability for programs that are high-Hispanic-serving with those that are low-Hispanic-serving (i.e., those programs for which less than 25 percent of the children enrolled are Hispanic).We used data from the National Survey of Early Care and Education (2012) to examine variation among these characteristics across three provider types: (1) center-based; (2) listed, home-based (generally including those providers who care for children with whom they have no prior relationship); and (3) unlisted, home-based (generally including relatives, friends, and neighbors who provide care to children with whom they had a prior relationship).(See data box for more information.)For simplicity, we refer to each of these types as providers.a In this series, we use the terms Hispanic and Latino interchangeably.b We use 25 percent as the cut-off for defining "high-Hispanic-serving" centers for two reasons.First, 1 in 4 children (25 percent) in the United States today is Hispanic.Second, higher cut-offs would result in the inclusion only of providers serving communities with large densities of Hispanic residents.High-Hispanic-serving indicates providers for which greater than 25 percent of the children enrolled are Hispanic.Low-Hispanic-serving refers to those providers for which less than 25 percent of the children enrolled are Hispanic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".