Precarious employment and the workplace transmission of COVID-19: evidence from workers’ compensation claims in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the association between precarious employment and risk of work-related COVID-19 infection in Ontario, Canada. METHODS: We combined data from an administrative census of workers' compensation claims with corresponding labour force statistics to estimate rates of work-related COVID-19 infection between April 2020 and April 2022. Precarious employment was imputed using a job exposure matrix capturing temporary employment, low wages, irregular hours, involuntary part-time employment and a multidimensional indicator of 'low', 'medium', 'high' and 'very high' overall exposure to precarious employment. We used negative binomial regression models to quantify associations between precarious employment and accepted compensation claims for COVID-19. RESULTS: We observed a monotonic association between precarious employment and work-related COVID-19 claims. Workers with 'very high' exposure to precarious employment presented a nearly fivefold claim risk in models controlling for age, sex and pandemic wave (rate ratio (RR): 4.90, 95% CI 4.07 to 5.89). Further controlling for occupational exposures (public facing work, working in close proximity to others, indoor work) somewhat attenuated observed associations. After accounting for these factors, workers with 'very high' exposure to precarious employment were still nearly four times as likely to file a successful claim for COVID-19 (RR: 3.78, 95% CI 3.28 to 4.36). CONCLUSIONS: During the first 2 years of the pandemic, precariously employed workers were more likely to acquire a work-related COVID-19 infection resulting in a successful lost-time compensation claim. Strategies aiming to promote an equitable and sustained recovery from the pandemic should consider and address the notable risks associated with precarious employment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".