Determinants of heat stress and strain in electrical utilities workers across North America as assessed by means of an exploratory questionnaire
Bibliographic record
Abstract
Previous field studies monitoring small groups of participants showed that heat stress in the electrical utilities industry may be detrimental to worker health and safety. Our aim in this study was to characterize heat stress and strain in electrical utilities workers across North America. A total of 428 workers in the power generation, transmission, and distribution industry across 16 U.S. states and 3 Canadian Provinces completed a two-part on-line questionnaire anonymously. The first part comprised 13 general questions on the employee’s workplace location, role in the organization, years of experience, general duties, average work shift duration, and other job-related information. It also included two questions on self-reported heat stress. The second part consisted of the “Heat Strain Score Index” (HSSI), a validated questionnaire which evaluates heat stress at the workplace as “safe level” (score ≤13.5: worker experiences no/low heat strain), “caution level” (score 13.6 to 18.0: moderate risk for heat strain), and “danger level” (score >18.0: high risk for heat strain). In addition to the survey, we obtained meteorological data from weather stations in proximity (12.3 ± 12.2 km) to the work locations. Based on the HSSI, 32.9%, 22.3%, and 44.4% of the responders’ workplaces were diagnosed as “safe level,” “caution level,” and “danger level,” respectively. The HSSI varied significantly depending on the occupation from 4.9 ± 3.2 in contact center workforce to 19.1 ± 5.4 in mechanics (p p p
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".