Is precarious employment an occupational hazard? Evidence from Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: To examine the association between precarious employment and risk of occupational injury or illness in Ontario, Canada. METHODS: We combined accepted lost-time compensation claims from the Workplace Safety and Insurance Board with labour force statistics to estimate injury and illness rates between January 2016 and December 2019. Precarious employment was imputed using a job exposure matrix and operationalised in terms of temporary employment, low wages, irregular hours, involuntary part-time employment and a multidimensional measure of 'low', 'medium', 'high' and 'very high' probabilities of exposure to precarious employment. Negative binomial regression models examined exposure to precarious employment in relation to risk of occupational injury or illness. RESULTS: After adjusting for age, sex and year, all indicators of precarious employment were associated with increased risk of injury or illness. Workers with 'high' and 'very' high' exposure to precarious employment presented a nearly threefold risk of injury or illness (rate ratio (RR): 2.81, 95% CI 2.73 to 2.89; RR: 2.82, 95% CI 2.74 to 2.90). Further adjustment for physical demands and workplace hazards attenuated associations, though a statistically and substantively significant exposure-outcome relationship persisted for workers with 'high' and 'very high' exposures to precarious employment (RR: 1.65, 95% CI 1.58 to 1.72; RR: 2.00, 95% CI 1.92 to 2.08). CONCLUSIONS: Workers exposed to precarious employment are more likely to sustain a lost-time injury or illness in Ontario, Canada. Workplace health and safety strategies should consider the role of precarious employment as an occupational hazard and a marker of work injury risk.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".