On the use of wearable physiological monitors to assess heat strain during occupational heat stress
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Workers in many industries are required to perform arduous work in high heat-stress conditions, which can lead to rapid increases in body temperature that elevate the risk of heat-related illness and even death. Traditionally, effort to mitigate work-related heat injury has been directed toward the assessment of environmental heat stress (e.g., wet-bulb globe temperature), rather than toward the associated physiological strain responses (e.g., heart rate and skin and core temperatures). However, because a worker's physiological response to a given heat stress is modified independently by inter-individual factors (e.g., age, sex, chronic disease, others) and intra-individual factors both within (e.g., medication use, fitness, acclimation and hydration state, others) and beyond (e.g., shift duration, illness, others) the worker's control, it becomes challenging to protect workers on an individual basis from heat-related injury without assessing those physiological responses. Recent advancements in wearable technology have made it possible to monitor one or more physiological indices of heat strain. Nonetheless, information on the utility of the wearable systems available for assessing occupational heat strain is unavailable. This communication is therefore directed toward identifying the physiological indices of heat strain that may be quantified in the workplace and evaluating the wearable monitoring systems available for assessing those responses. Finally, emphasis is placed on the barriers associated with implementing these devices to assist in mitigating work-related heat injury. This information is fundamental for protecting worker health and could also be utilized to prevent heat illnesses in vulnerable people during leisure or athletic activities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it