Practical Considerations for Using Personal Cooling Garments for Heat Stress Management in Physically Demanding Occupations: A Systematic Review and Meta‐Analysis Using Realist Evaluation
Bibliographic record
Abstract
ABSTRACT Introduction Due to rising temperature extremes, workplaces are seeking new solutions, such as using personal cooling garments (PCG) to mitigate and manage workplace heat exposure. This systematic review sought to assess the physiological and perceptual effects of PCGs on workers in standard work clothing performing moderate‐to‐heavy intensity tasks in hot environments. Methods A peer‐reviewed search strategy was conducted in MEDLINE, Embase, CINAHL, Scopus, Global Health, and Business Source Complete with no language or time limits. A meta‐analysis using a realist evaluation framework was then performed to evaluate the effectiveness of the PCGs. Results Thirty‐three studies with 764 participants (98% male; average 21 ± 34 participants per study), conducted primarily in a laboratory setting (76%) were included. The studies were 193 ± 190 min in duration and consisted of a moderate‐to‐heavy work effort of 3.3 ± 1.0 METs in hot ambient conditions (temperature: 35.9 ± 3.3°C, 51.4 ± 12.1% relative humidity, wet bulb globe temperature [WBGT] 31.2 ± 2.6°C). The PCGs (n = 67) facilitated heat exchange through conduction (n = 39), evaporation (n = 4), convection (n = 2), radiation (n = 2), or hybrid combinations (n = 20). Conductive and hybrid PCGs offered the greatest thermoregulatory benefit, whereby core temperature (Tc) and heart rate (HR) reductions were consistently observed (Conductive: Tc: −0.3°C, HR: −12 bpm; Hybrid: Tc:−0.2°C, HR: −10 bpm), while PCGs directed at enhancing evaporative and radiative heat exchange had no or minimal effect on the physiological outcomes assessed (i.e., TC < 0.1°C, HR: < 0.7 bpm). Conclusion While the PCGs had a positive overall effect, conductive options offered the most consistent benefit to workers. WBGT, clothing insulation, and duration of wear significantly affected some physiological and perceptual outcomes.
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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 | 0.047 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".