Indicators to assess physiological heat strain – Part 2: Delphi exercise
Bibliographic record
Abstract
In a series of three companion papers published in this Journal, we identify and validate the available thermal stress indicators (TSIs). In this second paper of the series, we identified the criteria to consider when adopting a TSI to protect individuals who work in the heat, and we weighed their relative importance using a Delphi exercise with 20 experts. Two Delphi iterations were adequate to reach consensus within the expert panel (Cronbach's α = 0.86) for a set of 17 criteria with varying weights that should be considered when adopting a TSI to protect individuals who work in the heat. 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. The 17 criteria were distributed across three occupational health-and-safety pillars: (i) contribution to improving occupational health (55% of total importance), (ii) mitigation of worker physiological strain (35.5% of total importance), and (iii) cost-effectiveness (9.5% of total importance). Three criteria [(i) relationship of a TSI with core temperature, (ii) having categories indicating the level of heat stress experienced by workers, and (iii) using its heat stress categories to provide recommendations for occupational safety and health] were considered significantly more important when selecting a TSI for protecting individuals who work in the heat, accumulating 37.2 percentage points. These 17 criteria allow the validation and comparison of TSIs that presently exist as well as those that may be developed in the coming years.
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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 | 0.114 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".