Effects of socio-economic factors on elementary school student COVID-19 infections in Ontario, Canada
Bibliographic record
Abstract
ABSTRACT Background The prevalence of SARS-CoV-2 infections in Ontario is disproportionately concentrated in areas with lower-income and racialized groups. We examined whether school-level and area-level socio-economic factors were associated with elementary school student infections in Ontario. Methods We performed multi-level modeling analyses using data from the Ministry of Education on school-based infections in Ontario in the 2020-21 school year and on school-level demographics, the Ontario Marginalization Index, and census data to estimate the variability of the cumulative incidence of SARS-CoV-2 infections amongst elementary school students attributable to individual schools (school level, Level 1) and forward sortation areas (FSAs) of schools (area level, Level 2). We explored whether socio-economic factors within individual schools and/or factors common to schools within FSAs predicted the incidence of elementary school student infections. Results At the school level, the proportion of students from low-income households within a school was positively related with the cumulative incidence of SARS-CoV-2 elementary school student infections ( β = .083, p < 0.001). At the area level, the dimensions of FSA marginalization were significantly related with cumulative incidence. Ethnic concentration ( β = .454, p < 0.001), residential instability ( β = .356, p < 0.001), and material deprivation ( β = .212, p < 0.001) were positively related. Area-related variables were more likely to explain variance in cumulative incidence than school-related variables (58% versus 1%, respectively). Interpretation Socio-economic characteristics of the geographic location of schools were more important in determining the cumulative incidence of SARS-CoV-2 elementary school student infections than individual school characteristics. Given inequitable effects of protracted education disruption, schools in marginalized areas should be prioritized for infection prevention measures and education continuity and recovery plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".