Population-Level Data on Child Development at School Entry Reflecting Social Determinants of Health: A Narrative Review of Studies Using the Early Development Instrument
Bibliographic record
Abstract
BACKGROUND: The Early Development Instrument (EDI) was developed as a population-level assessment of children's developmental health at school entry. EDI data collection has created unprecedented opportunities for population-level studies on children's developmental outcomes. The goal of this narrative review was to synthesize research using the EDI to describe how it contributes to expanding the understanding of the impacts of social determinants on child development and how it applies to special populations. METHODS: Select studies published in peer-reviewed scientific journals between 2015 and 2020 and incorporating the social determinants of health perspectives were chosen to highlight the capability of the EDI to monitor children's developmental health and contribute knowledge in the area of early childhood development. RESULTS: A number of studies have examined the association between several social determinants of health and children's developmental outcomes, including hard-to-reach and low-frequency populations of children. The EDI has also been used to evaluate programs and interventions in different countries. CONCLUSIONS: The ability of the EDI to monitor children's developmental outcomes in various populations has been consistently demonstrated. The EDI, by virtue of its comprehensive breadth and census-like collection, widens the scope of research relating to early childhood development and its social determinants of health.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".