Heat exposure and farmers' health in Vietnam: Impacts and health promotion strategies
Bibliographic record
Abstract
Increasing extremes of temperature is becoming a global public health threat. Exposure to high temperatures can result in heat-related illnesses (HRI), exacerbate existing chronic conditions, and increase the risk of hospitalisation and mortality. Agricultural workers, especially in developing countries, face heightened exposure risk due to prolonged outdoor exposure and insufficient protection. In Vietnam, a country where over one-third of its workforce engaged in farming and 90% of farmers relied on traditional manual farming methods, the heat-related health risks are even higher. Although agriculture is vital to the economy and food security, Vietnamese farmers lack protections from occupational health and safety (OHS) regulations and have limited access to health and social services. This PhD project aimed to evaluate the health effects of high-temperature exposure on Vietnamese farmers and developed health promotion strategies. The project included four studies that were connected and mutually supportive of each other. The first study conducted a global systematic review synthesising existing research on heat impacts on agricultural workers' health. The second involved a time-series analysis to examine the temperature-hospitalisation association of farmers in six Vietnamese provinces. The third study was a cross-sectional survey to estimate the prevalence of HRI symptoms and associated factors. Lastly, the fourth study implemented a community needs assessment (CNA) to explore different perspectives on heat exposure, heat-prevention difficulties, and prioritised solutions. These four studies were guided by a multiconceptual framework combining the Concept of Vulnerability, the Socioecological Model, the CNA framework, and the Ottawa Charter for Health Promotion. Together, these frameworks provided a comprehensive and context-sensitive approach to understanding the complex heat-health relationship. [...]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".