Occupational heat strain in outdoor workers: A comprehensive review and meta-analysis
Bibliographic record
Abstract
The present comprehensive review (i) summarizes the current knowledge on the impacts of occupational heat stress on outdoor workers, (ii) provides a historical background on this issue, (iii) presents a meta-analysis of published data, (iv) explores inter-individual and intra-individual factors, (v) discusses the available heat mitigation strategies, (vi) estimates physical work capacity, labour productivity, and metabolic rate for the year 2030, and (vii) provides an overview of existing policy and legal frameworks on occupational heat exposure. Meta-analytic findings from 38 field studies that involved monitoring 2,409 outdoor workers across 41 jobs in 21 countries suggest that occupational heat stress increases the core (r = 0.44) and skin (r = 0.44) temperatures, as well as the heart rate (r = 0.38) and urine specific gravity (r = 0.13) of outdoor workers (all p < 0.05). Moreover, it diminishes the capacity of outdoor workers for manual labour (r = -0.82; p < 0.001) and is responsible for more than two thirds of the reduction in their metabolic rate. Importantly, our analysis shows that physical work capacity is projected to be highly affected by the ongoing anthropogenic global warming. Nevertheless, the metabolic rate and, therefore, labour productivity are projected to remain at levels higher than the workers' physical work capacity, indicating that people will continue to work more intensely than they should to meet their financial obligations for food and shelter. In this respect, complementary measures targeting self-pacing, hydration, work-rest regimes, ventilated garments, and mechanization can be adopted to protect outdoor workers.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".