An Evaluation of Health and Safety Personnel and Workers' Occupational Heat Stress Knowledge in Ontario: Paper B
Bibliographic record
Abstract
INTRODUCTION: With the frequency and intensity of extreme heat events rising, occupational health and safety (OHS) personnel must be well-informed about heat management solutions. Consequently, assessing the knowledge level and technical expertise of OHS personnel is essential for equipping workplaces to operate safely in hotter conditions. METHODS: The Human and Environmental Physiology Research Unit-Occupational Heat Stress Knowledge Assessment Test (HEPRU-OHSKAT) was distributed among OHS personnel and the general working population (GWP) (> 18 years) throughout Ontario, Canada, to assess their current knowledge regarding managing occupational heat stress. The instrument included 31 items grouped into four core competency areas including: (A) General Heat Stress Knowledge (items (q): 8, max score (ms): 13), (B) Knowing the Signs and Symptoms of Heat Stress and First Aid (q: 7, ms: 32), (C) Exposure Limits and Heat Monitoring Practices (q: 13, ms: 40), and (D) Workplace Controls for Heat (q: 3, ms: 15). The number of respondents and the percentage of the total sample were calculated based on individual response rates to each question. RESULTS: A total of 317 respondents (female: 110, median age: 42 years, range: 18-74) from 17 industries (OHS: 68% vs. GWP: 32%) completed the HEPRU-OHSKAT. The average total knowledge score for all respondents was 54 ± 22% (56 ± 22% vs. 48 ± 21%), with no respondents exceeding 90% overall and scores on individual knowledge categories varying substantially. CONCLUSIONS: The HEPRU-OHSKAT identified knowledge gaps among OHS personnel in Ontario's workplaces, particularly for knowledge of control measures for reducing or preventing exposure to heat stress. PRACTICAL APPLICATIONS: Training and education are necessary across all HEPRU-OHSKAT knowledge categories to enhance heat stress management and minimize the OHS hazards associated with working in the heat.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".