Occupational Heat Risk Perceptions and Behavioral Adaptation Strategies Among Construction and Welding Workers in Bangladesh
Bibliographic record
Abstract
The increasing frequency and intensity of extreme heat events pose severe health risks to outdoor workers. Despite growing global recognition of occupational heat illness, evidence from low- and middle-income countries (LMICs) remains limited. This cross-sectional study surveyed 320 construction and welding workers to assess perceived heat-related health risk and behavioral adaptation in Bangladesh. Multinomial logistic regression examined factors associated with adaptive behaviors. Over 80% of workers perceived themselves as vulnerable, commonly reporting excessive sweating, thirst, cramps, irritability, and emotional instability. Construction workers were more likely to increase adaptive behaviors such as taking regular breaks (OR = 9.49, 95%CI: 2.45-36.74), wearing loose clothing (OR = 4.26, 95%CI: 1.14-15.90), and using electric fans (OR = 2.84, 95%CI: 1.12-7.22). However, they were also more likely to report a decrease in slowing their work pace (OR = 14.20, 95%CI: 2.03-99.21) and in planning work during cooler hours (OR = 34.81, 95%CI: 2.22-546.80). Long work experience was associated with increased use of electric fans as a cooling option (OR = 6.97, 95%CI: 1.97-24.68) and greater attention to weather forecasts (OR = 3.81, 95%CI: 1.01-14.37). Workers who experienced burns or memory decline adopted specific protective measures. Surprisingly, higher education was linked to lower participation in heat-safety training. These findings highlight the urgent need for occupational heat-safety policies, awareness campaigns, and targeted interventions to safeguard vulnerable outdoor workers in Bangladesh.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".