Physiological monitoring for occupational heat stress management: recent advancements and remaining challenges
Bibliographic record
Abstract
Occupational heat stress poses a major threat to worker health and safety that is projected to worsen with global warming. To manage these adverse effects, most industries rely on administrative controls (stay times and work-to-rest allocations) that are designed to limit the rise in body core temperature in the "average" individual. However, due to the extensive inter- and intra-individual variation in thermoregulatory function, these administrative controls will result in some individuals having their work rate and productivity unnecessarily restricted (false positives), while others may be subject to rises in heat strain that compromise health (false negatives). Physiological monitoring has long been touted as a more effective approach for individualized protection from excessive heat stress. This has led to extensive interest in the use of wearable technology for heat stress management from both the scientific community and manufacturers of wearable devices, which has accelerated in the past decade. In this review, we evaluate the merits of the recent and emerging approaches to manage occupational heat strain with wearable physiological monitors. Against this background, we then describe the issues that we perceive to be unresolved regarding the use of wearable heat strain monitors and the research efforts needed to address those issues. Particular emphasis is directed to the efficacy of existing physiological indicators of heat strain, how to define upper limits for those indicators and the efforts required to rigorously validate emerging wearable heat strain monitoring devices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".