Measuring Equity From The Start: Disparities In The Health Development Of US Kindergartners
Bibliographic record
Abstract
Racialized disparities in health and well-being begin early in life and influence lifelong health outcomes. Using the Early Development Instrument-a population-level early childhood health measure-this article examines potential health inequities with regard to neighborhood income and race/ethnicity in a convenience sample of 183,717 kindergartners in ninety-eight US school districts from 2010 to 2017. Our findings demonstrate a distinct income-related outcome gradient. Thirty percent of children in the lowest-income neighborhoods were vulnerable in one or more domains of health development, compared with 17 percent of children in higher-income settings. Significantly higher rates of income-related Early Development Instrument vulnerability-defined as children falling below the tenth-percentile cutoff on any Early Development Instrument domain-were demonstrated for Black/African American and Hispanic/Latinx children. These findings underscore the utility of the Early Development Instrument as a way for communities to measure child health equity gaps and inform the design, implementation, and performance of multisector place-based child health initiatives. More broadly, results indicate that for the US to make significant headway in decreasing lifelong health inequities, it is important to achieve health equity by early childhood.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".