Establishing a protocol for building a pan-Canadian population-based monitoring system for early childhood development for children with health disorders: Canadian Children’s Health in Context Study (CCHICS)
Bibliographic record
Abstract
INTRODUCTION: Health disorders early in life have tremendous impact on children's developmental trajectories. Almost 80% of children with health disorders lack the developmental skills to take full advantage of school-based education relative to 27% of children without a health disorder. In Canada, there is currently a dearth of nationally representative data on the social determinants of early childhood development for children with health disorders. Evidence from Canada and other countries indicate that poorer developmental outcomes in typically developing children are associated with lower socioeconomic status (SES). However, to date, it is not known whether this relationship is stronger among children with health disorders. The study's objectives are to estimate the prevalence and to investigate social determinants of developmental outcomes for young children with health disorders, using the Early Development Instrument (EDI). METHODS AND ANALYSIS: Study objectives will be achieved through three steps. First, using existing EDI data for 10 provinces and 2 territories collected from 2004 to 2015, we will investigate differences in developmental health outcomes among children with identified health disorders. Second, population-level EDI data will be linked with neighbourhood sociodemographic census data to explore associations between socioeconomic characteristics and rates of specific diagnoses among children aged 5-6 years, including trends over time. Third, for 3 of these 12 regions, additional health and/or education databases will be linked at an individual level. These data will be used to establish differences in EDI outcomes in relation to the age-of-onset of diagnosis, and presence of intervention or treatment. ETHICS AND DISSEMINATION: Study methodologies have been approved by the Hamilton Integrated Research Ethics Board. The results of the analyses of developmental health outcomes for children with health disorders combined with SES will have implications for both health service delivery and school-based intervention strategies. Results will contribute to a framework for public policy.
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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 | 0.108 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".