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Record W3012152656 · doi:10.1080/10803548.2020.1741251

The effect of cooling vests on physiological and perceptual responses: a systematic review

2020· review· en· W3012152656 on OpenAlexaff
Farideh Golbabaei, Ahad Heydari, Gholamreza Moradi, Habibollah Dehghan, Amirhossein Moradi, Peymaneh Habibi

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2020
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWorkloadChecklistRelative humidityClothingEnvironmental scienceEvaporative coolerPerceptionOccupational safety and healthComputer coolingMedicineAudiologyApplied psychologySimulationPsychologyComputer scienceEngineeringMeteorologyMechanical engineeringGeography

Abstract

fetched live from OpenAlex

Humans in hot environments are exposed to health risks and thermal discomfort which seriously affect their physical, physiological and mental workload. This study aimed to assess the effects of using cooling vests (CVs) on physiological and perceptual responses in the workplace. Three main databases were searched using subject headings and appropriate Mesh terms. The article has been written according to the preferred reporting items for systematic reviews checklist. A total of 23,837 studies were identified for screening and 63 studies were eligible for data extraction. A statistically significant difference was observed in body temperature among hybrid cooling garments (HBCGs), phase-change materials (PCMs) and air-cooled garments (ACGs) at 31.56–37 °C (60% relative humidity), evaporative cooling garments at 25.8–28.1 °C and liquid cooling garments at 35 °C (49% relative humidity) compared to without CVs (p < 0.001). HBCGs (PCMs and ACGs) are effective means in hot, moderate, humid or dry environments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.416
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.399
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2020
Admission routes1
Has abstractyes

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