Establishing heat stress indicators for work in a warming world: multi-country field evaluation and consensus recommendations
Bibliographic record
Abstract
BACKGROUND AND AIM: One third of the world labour force is frequently exposed to high heat stress at work, leading to significant health and labour implications. In a series of studies, we identified and validated the available thermal stress indicators (TSIs) for their capacity to protect individuals who work in the heat. METHODS: We conducted a systematic review to identify all TSIs and provide reliable information regarding their use. Then, we identified the criteria to consider when adopting a TSI and we weighed their relative importance using a Delphi exercise with 20 experts from 12 countries. Finally, we conducted field experiments across nine countries (372 workers during 893 full work shifts) to evaluate the efficacy of the meteorology-based TSIs for protecting individuals working in the heat. RESULTS: Our search identified 340 instruments and indicators developed between 200 BC and 2019 AD. Of these, 187 can be mathematically calculated utilizing only meteorological data. Of these meteorology-based TSIs, 127 were developed for people who are physically active, and 61 of those are eligible for use in occupational settings. Two Delphi iterations were adequate to reach consensus within the expert panel (Cronbach’s α=0.86) for 17 criteria with varying weights to be considered when adopting a TSI. These criteria considered physiological parameters such as core/skin/mean body temperature, heart rate, and hydration status, as well as practicality, cost effectiveness, and health guidance issues. In the third study, when evaluated against the 17 Delphi criteria, the 61 meteorology-based TSIs for occupational settings scored from 4.7 to 55.4%. The indoor (55.4%) and outdoor (55.1%) Wet-Bulb Globe Temperature and the Universal Thermal Climate Index (51.7%) scored higher compared to other TSIs (4.7-42.0%). CONCLUSIONS: We found that three TSIs are more efficacious and should be adopted to support evidence-based decision making and protect individuals who work in the heat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.537 | 0.468 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| 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".