8287721 Extreme weather events caused by climate change: estimating the prevalence of at-risk workers
Bibliographic record
Abstract
Rationale Climate change-related extreme weather events are projected to intensify yet there has been little research into the effects of these events on the health of workers. Through a literature review, we identified extreme heat, floods, droughts, and wildfires as priorities for study in Canada. We aim to assess the risk of mental and physical health effects from climate change-related extreme weather events on workers across Canada. Methods Employing CAREX Canada methods, we collected data on occupations and industries at risk of the extreme weather events indicated above. We used data from multiple sources to identify the population at risk: 2021 Canadian census, published literature, CAREX Canada estimates, and Canadian occupation databases. These were combined with health impacts and climate change predictions to create risk assessments for each occupation and industry. Results Estimates on workers at risk of heat stress as a result of occupational exposure to extreme heat will be presented. Occupations at risk include outdoor workers, as well as indoor workers in settings with inadequate ventilation. Workers who participate in high activity occupations, don protective equipment, and have less autonomy over workplace activities will have a higher hazard score among those exposed. Results will be presented by region in British Columbia, providing specific estimates for areas with greater exposure to extreme heat in the province, and nationwide by province. Conclusions The results from this study enhance our understanding of the health risks of climate change-related extreme weather events on workers. These results can provide crucial data that can lead to better protection of workers as climate change-related weather events become more common. CAREX Canada has a history of successful knowledge synthesis campaigns that will inform the dissemination of these data and will get the results to the audiences that will be most impactful.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".