Developing and Refining a Knowledge Assessment Instrument for Individuals Involved in Managing Occupational Heat Stress: Paper A
Bibliographic record
Abstract
INTRODUCTION: Occupational heat stress poses a critical threat to workers' health, safety, and productivity. To effectively manage this hazard, occupational health and safety (OHS) representatives must comprehensively understand heat stress, heat-associated injury and disease outcomes, and heat mitigation measures to protect workers. We developed the Human and Environmental Physiology Research Unit-Occupational Heat Stress Knowledge Assessment Test (HEPRU-OHSKAT) as an initial step toward facilitating research in this area. METHOD: The HEPRU-OHSKAT was developed using a mixed-methods approach. Preliminary items were developed after reviewing the academic and gray literature. A standardized content validity assessment was conducted with heat stress and OHS experts (n = 9); a trial distribution to refine and test feasibility was performed with an internal pilot group (n = 18). The instrument was then distributed to individuals involved with OHS (n = 216) and members of the general working population (n = 101) in Ontario, Canada (n = 317). Item analysis and item response theory modelling were used to refine the scale and scope of the instrument. RESULTS: Thirty-two items were developed for the preliminary instrument. Following expert consultation, a review of content validity, and the internal pilot, 31 items were retained in four subscales: General Heat Stress Knowledge (n = 8), Recognizing the Signs and Symptoms of Heat Stress and First Aid (n = 7), Exposure Limits and Heat Monitoring Practices (n= 13), and Workplace Controls for Heat (n = 3). Following item selection, the broader instrument was reduced to 20 items grouped into one scale. CONCLUSIONS: The HEPRU-OHSKAT is the first instrument to assess knowledge of heat stress among those responsible for OHS. The instrument showed good reliability and internal consistency across knowledge categories. PRACTICAL APPLICATIONS: The instrument can help OHS representatives better evaluate knowledge of, and manage training about, heat stress.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".