370 Can compensation statistics detect the impact of summer outdoor temperatures on workers’ health and safety? Preliminary results in Quebec (Canada)
Bibliographic record
Abstract
Objectives Increased temperatures associated with climate change are likely to have impacts on occupational health and safety all over the world. We aimed to explore potential relationships between summer outdoor temperatures and occupational compensation statistics for heat-related morbidity and mortality. Methods Daily compensation counts in the region of Montreal for heat-related health outcomes (such as heat strain, heatstroke, loss of consciousness) were obtained from the workers’ compensation board of Quebec for the months of May to September over the period 2000–2010. Daily summer outdoor temperatures for the study period were obtained from Environment Canada. Associations between daily compensation counts and temperatures were analysed with regular Poisson and negative binomial regression models. Results There were 35 compensations for heat-related health outcomes during the 11-year period (for a working population of approximately 1.85 million). Incidence rate ratio (IRR) obtained from preliminary Poisson regression analyses was 1.76 (95% CI: 1.55–2.00) per 1oC temperature increase. This large IRR translates into a small increase in compensations, given the low compensation base rate (0.002 compensation per day for heat-related health problems) at the average temperature of 18.4 oC. Virtually identical results were obtained with a negative binomial regression. Analyses will be carried out for other regions of Quebec and for indirect impacts of heat (e.g. accidents/injuries related to fatigue and lack of vigilance), with various metrics of temperature (e.g. maximum and minimum, Wet Bulb globe Index), and will be stratified by industrial sectors, age and sex when possible. Conclusions These preliminary results suggest that the effect of increases in summer temperatures can be detected in compensation statistics. The results of this work could prove useful for the surveillance of current and future occupational health and safety risks associated with outdoor temperatures and to orient interventions.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".