Workplace heat exposure, health protection, and economic impacts: A case study in Canada
Bibliographic record
Abstract
BACKGROUND: Occupational heat exposure is a serious concern for worker health, productivity, and the economy. Few studies in North America assess how on-site wet bulb globe temperature (WBGT) levels and guidelines are applied in practice. METHODS: We assessed the use of a WBGT sensor for localized summertime heat exposures experienced by outdoor laborers at an industrial worksite in Ontario, Canada during the warm season (May-October) from 2012 to 2018 inclusive. We further examined informed decision making, approximated workers' predicted heat strain (sweat loss, core temperature), and estimated potential financial loss (via hourly wages) due to decreased work allowance in the heat. RESULTS: Significantly higher worksite WBGT levels occured compared with regional levels estimated at the airport, with an upward trend in heat warnings over the 7 years and expansion of warnings into the fall season. The maximum WBGT during warnings related strongly to predicted hourly sweat loss. On average, 22 hours per worker were lost each summer (~1% of annual work hours) as a result of taking breaks or stopping due to heat. This amount of time corresponded to an average individual loss of C$1100 Canadian dollars (~C$220,000 combined for ~200 workers) to workers or the company. The additional losses for an enterprise due to reduced product output were not estimated. CONCLUSIONS: Worksite observations and actions at the microscale are essential for improving the estimates of health and economic costs of extreme heat to enterprises and society. Providing worksite heat metrics to the employees aids in appropriate decision making and health protection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".