Considerations for occupational heat exposure: A scoping review
Bibliographic record
Abstract
The ability to regulate core body temperature is a critical factor in avoiding occupational heart stress in demanding environments. Heat-related illness in an occupational setting is complex and multifactorial and includes environment (intrinsic and extrinsic), the occupational clothing requirements and physiological factors. Much of this research began in the gold mines in South Africa after several miners died due to heat related illness. Similar research was conducted during World War Two and was crucial for the creation of acclimatization techniques and strategies for acquiring thermal tolerance. Techniques such as fatigue recovery and body cooling are still used today to prevent heat related illness in individuals with occupations that have frequent exposure to heat and high physical loads. These individuals are at greater risk of heat related illness as extended exposure to a hot or humid environment in combination with strenuous physical activity can overwhelm the body’s homeostatic cooling mechanisms. In addition, individuals from special populations with chronic or acute health impacts such as diabetes mellitus, also have a greater risk for the aforementioned. Currently, there are several heat prevention strategies, including training and education, regulation and monitoring, in place to protect workers from heat related illness and casualty. These strategies, along with future considerations and the impact of climate change will be highlighted in this review.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".