MétaCan
Menu
Back to cohort
Record W4412200547 · doi:10.3390/atmos16070839

Climate Change and Occupational Risks in Outdoor Workers: A Systematic Review of the Health Effects of Extreme Temperatures

2025· review· en· W4412200547 on OpenAlexaboutno aff
Maria Francesca Rossi, Raimondo Leone, Umberto Moscato

Bibliographic record

VenueAtmosphere · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEnvironmental scienceEnvironmental healthExtreme heatClimatologyMedicineGeologyOceanography

Abstract

fetched live from OpenAlex

Climate change is one of the most important current threats to global health. Outdoor workers are among the most vulnerable people to its effects. The aim of this systematic review is to assess the occupational risks related to climate change, investigating health outcomes in outdoor workers and estimating its impact in the occupational context. The review was performed following PRISMA guidelines, screening three databases (PubMed, Web of Science, and Scopus). Studies written in English or Italian languages, performed on outdoor workers, assessing occupational risks linked to climate change, and reporting on health outcomes were included. A quality assessment was performed using the Newcastle–Ottawa Scale. Thirteen studies were included in the review, performed mostly on construction (seven studies, 53.8%) and agricultural (five studies, 38.5%) workers. Twelve of the included studies (92.3%) reported on occupational risks related to heat stress, one on the effects of cold weather. Four studies (30.8%) reported a high prevalence of heat-related symptoms, ranging from 64.0% to 90.3% of workers. This systematic review highlights heat-related stress in outdoor workers as an important occupational risk, but it also underlines an important gap in scientific knowledge regarding other occupational risks relating to climate change.

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)0.000
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 score0.043
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.128
GPT teacher head0.387
Teacher spread0.259 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAtmosphereSame topicClimate Change and Health ImpactsFrench-language works237,207