International Migrant Workers, Heat Exposure, and Climate Change: A Systematic Review of Health Risks and Protective Interventions
Bibliographic record
Abstract
Abstract Background International migrant workers, representing 170 million people globally, often face hazardous working conditions, including extreme heat exposure. This increases their risk of occupational heat strain, exacerbated by poor working conditions. This systematic review aims to identify the health risks of occupational heat exposure among international migrant workers globally, and document existing protective interventions and measures, in order to inform policies that protect this vulnerable population. Methods We searched four electronic databases (Medline, Embase, Ovid Global Health and PsychINFO) for primary research studies (January 2014–April 2024) on international migrant workers experiencing adverse health outcomes following high working temperatures. Records were screened, and data extracted by two independent reviewers. Assessment of study quality was done using Joanna-Briggs Institute checklists. Results were synthesised narratively and reported following PRISMA 2020 guidelines. The protocol was registered in PROSPERO (CRD42024519547). Results Of the 646 records screened, 19 studies involving 2,322 migrant workers across six countries were included in the analysis, with most studies from high-income countries (n=14, 74%), mainly the USA. Studies focused on workers in construction (48%) and agriculture (42%), with migrant workers originating from 14 countries, predominantly India, Mexico, and Nepal. Reported health outcomes included heat-related illnesses (n=12), dehydration (n=5), kidney disease (n=2), and poor skin health (n=2). Common symptoms included headaches, muscle cramps, and heavy sweating. Interventions focused on water, rest, shade, skin protection, and education, but evaluations were limited and some measures failed to address heat exposure effectively. Conclusions Occupational heat exposure poses significant health risks for international migrant workers. Where interventions exist, barriers to effectiveness remain, with a knowledge gap as to the situation in low- and middle-income countries. Amid rising global temperatures improved worker education, worker-tailored and co-designed interventions, updated protocols, and increased healthcare accessibility are urgently needed.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".