An exploratory survey of heat stress management programs in the electric power industry
Bibliographic record
Abstract
Workers in the electric power industry commonly perform physically demanding jobs in hot environments, which combined with the protective clothing worn, places them at risk of disease and health problems related to occupational heat stress. With climate change fueling an increase in the occurrence of hot weather, a targeted approach to heat stress management within the industry is needed. To better understand current heat management practices and identify opportunities for refinement, we conducted an exploratory survey among 33 electric utility companies operating in the United States (n = 32) and Canada (n = 1). Forty-six employees responsible for health and safety of company workers completed 26 questions assessing heat stress as a workplace hazard and heat management practices within five categories: (1) use and administration of heat stress management program; (2) surveillance of heat stress and heat strain; (3) job evaluation and heat-mitigation guidance; (4) education and training programs; and (5) treatment of heat-related illness. While a majority of the respondents (87.0%) indicated heat stress is a workplace hazard and their organization has a heat stress management program (78.3%), less than half reported performing real-time monitoring of heat stress in the workplace (47.8%) or tracking worker heat strain (19.6%) (i.e., physiological response to heat stress). However, most organizations indicated they conducted pre-job evaluations for heat stress (71.7%) and implemented an employee training program on managing heat stress (73.9%). The latter included instruction on various short- and long-term heat-mitigation guidance for workers (e.g., use of work exposure limits, hydration protocols) and the prevention (52.2%) and treatment (63.1%) of heat-related illnesses. Altogether, our survey demonstrates that although many companies employ some form of a heat management program, the basic components defining the programs vary and are lacking for some companies. To maximize worker health and safety during work in hot environments, a consensus-based approach, which considers the five basic components of a heat management program, should be employed to formulate effective practices and methodologies for creating an industry-specific heat management strategy.
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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 | 0.004 |
| 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 | 0.000 |
| 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".