An exploratory survey of on-site heat stress management practices in the Canadian mining industry
Bibliographic record
Abstract
With climate change fueling more frequent and intense periods of hot weather, heat stress management programs are becoming increasingly important for protecting the health and safety of workers in the Canadian mining industry. While the inclusion of heat-mitigation measures such as those provided by the American College of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) are commonly employed by industry, there is a need to develop more comprehensive industry-specific measures for heat stress prevention and management. To better understand current heat management practices and identify opportunities for improvement, an exploratory survey of 51 employees responsible for health and safety at underground mining (n = 35), and surface operations (n = 16) (e.g., open-pit mining, milling, smelting, and exploration site) was conducted in Canada. The respondents answered 50 questions related to workplace heat stress management, including descriptors of the workplace environment, perceived heat stress hazard, administration of heat stress management programming, heat stress emergency procedures, environmental monitoring strategies, and knowledge of mining-specific regulations related to heat stress. Twenty-four managers (47%) reported that heat-related illnesses led to restricted duty or lost time claims at their site, with a median of 5 [IQR: 2–10, max: 30] reportable heat-related illnesses occurring per site annually. Many also felt that heat-related illnesses are under-reported by their workforce (n = 36, 71%). Most sites reported established heat stress management programs to prevent heat illness (n = 43, 84%), typically based on the TLVs (n = 38, 75%). Although some organizations do conduct pre-task evaluations for heat stress (n = 30, 59%), more than half do not conduct post-job evaluations (n = 28, 55%) or pre-employment screening for heat stress vulnerability (n = 3, 6%). While our findings indicate that the health and safety managers recognize the hazard posed by heat and have stated practices to help address the hazard, we also observed inconsistencies in heat stress management programming across the sample. Developing and adopting a standard heat stress management and reporting system would be an important step toward protecting workers from existing and emerging threats from extreme heat and climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".