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Record W4393858297 · doi:10.1016/s2542-5196(24)00030-5

A critical review of the effectiveness of electric fans as a personal cooling intervention in hot weather and heatwaves

2024· review· en· W4393858297 on OpenAlexafffund
Robert D. Meade, Sean R. Notley, Nathalie V. Kirby, Glen P. Kenny

Bibliographic record

VenueThe Lancet Planetary Health · 2024
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchHealth CanadaUniversity of the Witwatersrand, Johannesburg
KeywordsEvaporative coolerEnvironmental scienceAir conditioningClimate changeIntervention (counseling)MeteorologyEngineeringPsychologyGeographyMechanical engineering

Abstract

fetched live from OpenAlex

Health agencies worldwide have historically cautioned that electric fans accelerate body-heat gain during hot weather and heatwaves (typically in air temperatures ≥35°C). However, guidance published since 2021 has suggested that fans can still cool the body in air temperatures up to 40°C by facilitating sweat evaporation, and therefore are an inexpensive yet sustainable alternative to air conditioning. In a critical analysis of the reports cited to support this claim, we found that although fan use improves sweat evaporation, these benefits are of insufficient magnitude to exert meaningful reductions in body core temperature in air temperatures exceeding 35°C. Health agencies should continue to advise against fan use in air temperatures higher than 35°C, especially for people with compromised sweating capacity (eg, adults aged 65 years or older). Improving access to ambient cooling strategies (eg, air conditioning or evaporative coolers) and minimising their economic and environmental costs through policy initiatives, efficient cooling technology, and combined use of low-cost personal interventions (eg, skin wetting or fan use) are crucial for climate adaptation.

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)0.002
metaresearch head score (Gemma)0.001
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.307
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.085
GPT teacher head0.423
Teacher spread0.338 · 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

Citations52
Published2024
Admission routes2
Has abstractyes

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