Climate Change and Occupational Health and Safety in a Temperate Climate: Potential Impacts and Research Priorities in Quebec, Canada
Bibliographic record
Abstract
The potential impacts of climate change (CC) on Occupational Health and Safety (OHS) have been studied a little in tropical countries, while they received no attention in northern industrialized countries with a temperate climate. This work aimed to establish an overview of the potential links between CC and OHS in those countries and to determine research priorities for Quebec, Canada. A narrative review of the scientific literature (2005-2010) was presented to a working group of international and national experts and stakeholders during a workshop held in 2010. The working group was invited to identify knowledge gaps, and a modified Delphi method helped prioritize research avenues. This process highlighted five categories of hazards that are likely to impact OHS in northern industrialized countries: heat waves/increased temperatures, air pollutants, UV radiation, extreme weather events, vector-borne/zoonotic diseases. These hazards will affect working activities related to natural resources (i.e. agriculture, fishing and forestry) and may influence the socioeconomic context (built environment and green industries), thus indirectly modifying OHS. From this consensus approach, three categories of research were identified: 1) Knowledge acquisition on hazards, target populations and methods of adaptation; 2) Surveillance of diseases/accidents/occupational hazards; and 3) Development of new occupational adaptation strategies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".