A Pan-Canadian Data Resource for Monitoring Child Developmental Health: The Canadian Neighbourhoods Early Child Development (CanNECD) Database
Bibliographic record
Abstract
The Canadian Neighbourhoods and Early Child Development (CanNECD) database is a unique resource for research on child developmental health and well-being within the socioeconomic and cultural context of Canadian neighbourhoods. This paper describes the CanNECD database and highlights its potential for advancing research at the intersection of child development, social determinants of health, and neighbourhood effects. The CanNECD database contains cross-sectional population-level child developmental health data from all across Canada collected through regional implementation of the Early Development Instrument (EDI), geo-coded information on residential neighbourhoods covering all of Canada, and socioeconomic and demographic variables from the Canada Census and Income Taxfiler database. Individuals are not identified in the database, as no identifying information, such as names and addresses, is attached to the EDI record. At data collection, each individual child is given a unique number which is a combination of site, school, and position on a class list. Each neighbourhood receives a unique identifier which then is linkable across datasets. The nearly 800,000 EDI records spanning 2003-2014 and representing all Canadian provinces and territories (with the exception of Nunavut) are compiled in a secure electronic collection system at the Offord Centre for Child Studies, McMaster University in Hamilton, Canada. Early studies using the EDI demonstrated its utility as a tool for assessing child developmental health at a population level, and its potential for both community-level and large-scale monitoring of child populations. Research using the CanNECD database is now examining to what extent social determinants and the steepness of the social gradients of developmental health differ between geographical jurisdictions and between different sub-populations. We are also working to identify outlier neighbourhoods in which EDI scores are substantially higher or lower than predicted by a neighbourhood's demographic and socioeconomic characteristics, and exploring other potentially important determinants of children's developmental health. Finally, we are examining the extent to which change-over-time in aggregate EDI scores varies geographically, and how well it coincides with changes in socioeconomic factors. Thus, the CanNECD database offers the opportunity for research that will inform national policies and strategies on child developmental 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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.035 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".