Agreement between measured and self‐reported physiological strain in males and females during simulated occupational heat stress
Bibliographic record
Abstract
RATIONALE: Monitoring physiological strain is recommended to safeguard workers during heat exposure, but is logistically challenging. The perceptual strain index (PeSI) is a subjective estimate thought to reflect the physiological strain index (PSI) that requires no physiological monitoring. However, sex is known to influence perceptions of heat stress, potentially limiting the utility of the PeSI. OBJECTIVES: The objective of this study was to assess whether sex modifies the relationship between PeSI and PSI. METHODS: ) for 180 min or until termination (volitional fatigue, rectal temperature ≥39.5°C) in 16°C, 24°C, 28°C, and 32°C wet-bulb globe temperatures. Rectal temperature and heart rate were recorded to calculate PSI (0-10 scale). Rating of perceived exertion and thermal sensation were recorded to calculate PeSI (0-10 scale). Relationships between PSI and PeSI were evaluated via linear mixed models. Mean bias (95% limits of agreement [LoA]) between PSI and PeSI was assessed via Bland-Altman analysis. Mean absolute error between measures was calculated by summing absolute errors between the PeSI and the PSI and dividing by the sample size. FINDINGS: PSI increased with PeSI (p < 0.01) but the slope of this relation was not different between males and females (p = 0.83). Mean bias between PSI and PeSI was small (-0.4 points), but the 95% LoA (-3.5 to 2.7 points) and mean absolute error were wide (1.3 points). IMPACT: Our findings indicate that sex does not appreciably impact the agreement between the PeSI and PSI during simulated occupational heat stress. The PeSI is not a suitable surrogate for the PSI in either male or female workers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".