Variation in occupational exposure risk for COVID-19 workers’ compensation claims across pandemic waves in Ontario
Bibliographic record
Abstract
OBJECTIVES: To understand rates of work-related COVID-19 (WR-C19) infection by occupational exposures across waves of the COVID-19 pandemic in Ontario, Canada. METHODS: We combined workers' compensation claims for COVID-19 with data from Statistics Canada's Labour Force Survey, to estimate rates of WR-C19 among workers spending the majority of their working time at the workplace between 1 April 2020 and 30 April 2022. Occupational exposures, imputed using a job exposure matrix, were whether the occupation was public facing, proximity to others at work, location of work and a summary measure of low, medium and high occupational exposure. Negative binomial regression models examined the relationship between occupational exposures and risk of WR-C19, adjusting for covariates. RESULTS: Trends in rates of WR-C19 differed from overall COVID-19 cases among the working-aged population. All occupational exposures were associated with increased risk of WR-C19, with risk ratios for medium and high summary exposures being 1.30 (95% CI 1.09 to 1.55) and 2.46 (95% CI 2.10 to 2.88), respectively, in fully adjusted models. The magnitude of associations between occupational exposures and risk of WR-C19 differed across waves of the pandemic, being weakest for most exposures in period March 2021 to June 2021, and highest at the start of the pandemic and during the Omicron wave (December 2021 to April 2022). CONCLUSIONS: Occupational exposures were consistently associated with increased risk of WR-C19, although the magnitude of this relationship differed across pandemic waves in Ontario. Preparation for future pandemics should consider more accurate reporting of WR-C19 infections and the potential dynamic nature of occupational exposures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".