Pre- versus post-COVID-19 pandemic comparison of kindergarten teacher-reported child development in multiple Canadian jurisdictions
Bibliographic record
Abstract
Little is known so far about the impact of the COVID-19 pandemic on young children. We assessed the effect of the pandemic on pre-existing developmental trends in this population-level, repeated cross-sectional cohort design study of child development at school entry, measured with the kindergarten teacher-completed Early Development Instrument (EDI) in the 10 years prior to the onset of the COVID-19 pandemic and two years after. Individual EDI data for 913,739 children with individual EDI records were aggregated to 1,398 neighbourhoods in 8 of Canada’s 13 provinces and territories and grouped into four time intervals (three pre-pandemic and one post-pandemic) including all jurisdictions. The COVID-19 pandemic was the main exposure, and overall vulnerability on the EDI (scoring below normative threshold in one or more of the 5 developmental domains) was the primary outcome. Demographic (e.g., age) and neighbourhood-level socioeconomic (SES) characteristics were investigated as potential modifiers– specifically, whether poverty and COVID-19 suggested a double jeopardy effect. Latent growth curve models with structured residuals were used to quantify whether post-COVID-19 vulnerability rates deviated from the pre-COVID-19 trajectory. Overall vulnerability rates were increasing by 0.39% per year prior to the onset of the pandemic. On average, post-COVID-19 developmental vulnerability rates did not deviate from this pre-COVID-19 trajectory. Demographic variables predicted post-COVID-19 deviations, whereas neighbourhood SES did not. However, neighbourhood SES moderated the effects of some demographic variables. As society continues to grapple with the consequences of the COVID-19 pandemic impact, these results underscore the continuing need of monitoring child development and education trends. • Neighborhood-level developmental vulnerability in Canada was increasing pre-COVID-19 • In most developmental domains, this trend continued post-COVID-19 onset • Neighborhood SES did not predict post-COVID-19 deviations, but demographics did • In poorer neighborhoods with younger children, vulnerability rates increased more • Vulnerability increased more in wealthier neighborhoods with less language fluency
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".