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Record W7064710394

Climate Change and Outdoor Workers: A Narrative Review

2025· article· en· W7064710394 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeGlobeNarrative reviewHeat stressAir temperatureClimate modelGlobal warmingAgricultureEffects of global warmingIndex (typography)
DOInot available

Abstract

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Nassiri P, Monazzam MR, Golbabaei F, Shamsipour A, Arabalibeik H, Mortezapour AR, et al Applicability of Modified discomfort index (MDI) in Outdoor occupational environments: a case study of an open pit mines in Tehran Province. Iran Occupational Health. 2018;15(1):136-45. Asghari M, Ghalhari GF, Abbasinia M, Shakeri F, Tajik R, Ghannadzadeh MJ. Feasibility of relative strain index (RSI) for the assessment of heat stress in outdoor environments: Case study in three different climates of Iran. The Open Ecology Journal. 2020;13(1):11-18. Bonafede M, Marinaccio A, Asta F, Schifano P, Michelozzi P, Vecchi S. The association between extreme weather conditions and work-related injuries and diseases. A systematic review of epidemiological studies. Annali dell'Istituto superiore di sanita. 2016;52(3):357-67. Narocki C. Heatwaves as an Occupational Hazard: The Impact of Heat and Heatwaves on Workers’ Health, Safety and Wellbeing and on Social Inequalities. ETUI Research Paper-Report. 2021. 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The development of an empirical model for estimation of the sensitivity to heat stress in the outdoor workers at risk. Annals of Medical and Health Sciences Research. 2017;7(2):77-84. Hahn MB, Riederer AM, Foster SO. The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique. Global environmental change. 2009;19(1):74-88. Tawatsupa B, Lim LL, Kjellstrom T, Seubsman S-a, Sleigh A, Team TCS. Association between occupational heat stress and kidney disease among 37 816 workers in the Thai Cohort Study (TCS). Journal of epidemiology. 2012;22(3):251-60. Mathee A, Oba J, Rose A. Climate change impacts on working people (the HOTHAPS initiative): findings of the South African pilot study. Global health action. 2010;3(1):5612. Kiatkitroj K, Arphorn S, Tangtong C, Maruo SJ, Ishimaru T. Risk factors associated with heat-related illness among sugarcane farmers in Thailand. Industrial health. 2021;60(5):447-58. 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Heat stress perception among native and migrant workers in Italian industries—case studies from the construction and agricultural sectors. International journal of environmental research and public health. 2019;16(7):1090. Amoadu M, Ansah EW, Sarfo JO, Hormenu T. Impact of Climate Change and Heat Stress on Workers’ Health and Productivity: A Scoping Review. The Journal of Climate Change and Health. 2023:100249. Boonruksa P, Maturachon T, Kongtip P, Woskie S. Heat stress, physiological response, and heat-related symptoms among Thai sugarcane workers. International journal of environmental research and public health. 2020;17(17):6363. Wesseling C, Aragón A, González M, Weiss I, Glaser J, Rivard CJ, et al. Heat stress, hydration and uric acid: a cross-sectional study in workers of three occupations in a hotspot of Mesoamerican nephropathy in Nicaragua. BMJ open. 2016;6(12):e011034. Lao J, Hansen A, Nitschke M, Hanson-Easey S, Pisaniello D. 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Moyce S, Mitchell D, Armitage T, Tancredi D, Joseph J, Schenker M. Heat strain, volume depletion and kidney function in California agricultural workers. Occupational and environmental medicine. 2017;74(6):402-9. Foster J, Smallcombe JW, Hodder S, Jay O, Flouris AD, Nybo L, et al. An advanced empirical model for quantifying the impact of heat and climate change on human physical work capacity. International Journal of Biometeorology. 2021;65:1215-29. Butler-Dawson J, Krisher L, Asensio C, Cruz A, Tenney L, Weitzenkamp D, et al. Risk factors for declines in kidney func

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.241
GPT teacher head0.541
Teacher spread0.299 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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