Neighborhood-level sociodemographics and kindergarten children’s developmental vulnerability, pre- and post-COVID-19 in Canada
Bibliographic record
Abstract
A child’s environment and early life experiences play an important role in shaping their development. The COVID-19 pandemic altered many aspects of everyday life for young children in Canada, and it has been argued that these changes did not impact all children equally. The current study explored the association between early child development and neighborhood sociodemographic indices (e.g., income, deprivation) before and after the onset of the COVID-19 pandemic. The Early Development Instrument (EDI) data, measuring child development and school readiness in kindergarten, were collected in 7 Canadian provinces and 1 territory from 2017-2020 (pre-COVID) and 2020-2023 (post-COVID) and were linked with neighborhood-level socioeconomic indices from 2016. We compared the gradients in development in a pre- (n=293,700) and a post-COVID (n=246,305) cohort of Canadian children. Rates of developmental vulnerability, as measured by the EDI, were examined for the sociodemographic variables in both the pre- and post-COVID cohorts. The overall vulnerability increased nationally from 27.3% in the pre- to 28.0% in the post-COVID cohort. A gradient in vulnerability rates for all the socioeconomic indices was found, with children in the highest quintiles, representing greater deprivation, having greater odds of being vulnerable than their peers in the lowest, least deprived quintile. Contrary to expectation, the magnitude of the gradient was very similar in both cohorts, with some jurisdictional differences. The largest gradient was found for average neighborhood income. These findings add the context of social determinants of health to the understanding of the impact of the COVID-19 pandemic on young children.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".