Climate Change and Occupational Heat Strain Among Women Workers: A Systematic Review
Bibliographic record
Abstract
Climate change increases heat stress exposure and occupational heat strain in tropical and subtropical regions with generally hot-humid climate conditions. The present systematic review was conducted to assess the effect of climate change on occupational heat strain among women workers. In this study, three main databases (PubMed, Scopus, and Web of Science) were searched to find relevant literature on climate change and its effects using subject headings and appropriate MeSh terms. This article has been written according to the PRISMA checklist. A total of 6,176 studies were identified for screening and 13 studies were eligible for data extraction. Scientific evidence reveals that there is an imprecise but positive relationship between climate change and occupational heat strain regarding women workers. Some complications associated with occupational heat strain among women workers include fatigue, discomfort, dehydration, reduced brain function, and loss of concentration. Climate change can lead to an increase in the occurrence of heat-related illnesses and the levels of injury risk. In addition, its adverse health effects on women workers are mentioned. This systematic study identifies key priorities for action to better characterize and understand how occupational heat strain among women workers may be associated with climate change events. Strong evidence indicates that climate change will continue to cause occupational heat strain among women workers. It is essential to implement preventive measures considering multidisciplinary strategies to reduce the adverse effects of climate change on women workers health in hot weather settings. This can limit the health risks and negative effects of climate change.
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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) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".