Socioeconomic gradient in the developmental health of Canadian children with disabilities at school entry: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between developmental health and neighbourhood socioeconomic status (SES) in kindergarten children with disabilities. DESIGN: Cross-sectional study using population-level database of children's developmental health at school entry (2002-2014). SETTING: 12 of 13 Canadian provinces/territories. MEASURES: Taxfiler and Census data between 2005 and 2006, respectively, were aggregated according to custom-created neighbourhood boundaries and used to create an index of neighbourhood-level SES. Developmental health outcomes were measured for 29 520 children with disabilities using the Early Development Instrument (EDI), a teacher-completed measure of developmental health across five domains. ANALYSIS: Hierarchical generalised linear models were used to test the association between neighbourhood-level SES and developmental health. RESULTS: All EDI domains were positively correlated with the neighbourhood-level SES index. The strongest association was observed for the language and cognitive development domain (β (SE): 0.29 (0.02)) and the weakest association was observed for the emotional maturity domain (β (SE): 0.12 (0.01)). CONCLUSIONS: The magnitude of differences observed in EDI scores across neighbourhoods at the 5th and 95th percentiles are similar to the effects of more established predictors of development, such as sex. The association of SES with developmental outcomes in this population may present a potential opportunity for policy interventions to improve immediate and long-term outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".