Occupational health effects during periods with heat
Bibliographic record
Abstract
Abstract Hydration status, awareness and the attitude to health and performance effects of preventing dehydration was evaluated in five industries across Europe. The prevalence of dehydration was assessed via analyses of urine samples from 139 workers and questionnaires collected from employers as well as employees collected from ten different work places. In total 80 % of all workers were either suboptimal hydrated at the onset of work or became dehydrated during their work shift with levels equal to or higher than those associated with impaired function in cognitively dominated tasks and complex motor function. The high prevalence of dehydration is in conflict with ∼75% of all workers that emphasize drinking as the most important mitigation strategy during periods with elevated heat strain. Also, work safety and prevention of negative health effect was stressed as very important both by employers and employees (average score 9 out of 10). Although, hydration is emphasized by work-safety advisories, it seems clear that more effective 24/7 hydration strategies are warranted. We propose that future protection of workers against detrimental effects of heat should consider personalized alerts that can integrate the importance of timing to facilitate the development of appropriate hydration habits that accounts for the large inter-individual variation in sweating and hence differences in water and electrolyte needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".