MétaCan
Menu
Back to cohort
Record W4386485723 · doi:10.1371/journal.pclm.0000202

Considerations for occupational heat exposure: A scoping review

2023· review· en· W4386485723 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePLOS Climate · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsMcGill University Health CentreUniversité de MontréalMcGill UniversityUniversity of TorontoMcMaster University
Fundersnot available
KeywordsHeat illnessMedicineHeat stressEnvironmental healthHeat exhaustionClothingAcute illnessPoison controlPolitical scienceGeography

Abstract

fetched live from OpenAlex

The ability to regulate core body temperature is a critical factor in avoiding occupational heart stress in demanding environments. Heat-related illness in an occupational setting is complex and multifactorial and includes environment (intrinsic and extrinsic), the occupational clothing requirements and physiological factors. Much of this research began in the gold mines in South Africa after several miners died due to heat related illness. Similar research was conducted during World War Two and was crucial for the creation of acclimatization techniques and strategies for acquiring thermal tolerance. Techniques such as fatigue recovery and body cooling are still used today to prevent heat related illness in individuals with occupations that have frequent exposure to heat and high physical loads. These individuals are at greater risk of heat related illness as extended exposure to a hot or humid environment in combination with strenuous physical activity can overwhelm the body’s homeostatic cooling mechanisms. In addition, individuals from special populations with chronic or acute health impacts such as diabetes mellitus, also have a greater risk for the aforementioned. Currently, there are several heat prevention strategies, including training and education, regulation and monitoring, in place to protect workers from heat related illness and casualty. These strategies, along with future considerations and the impact of climate change will be highlighted in this review.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.005

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 head0.480
GPT teacher head0.474
Teacher spread0.006 · 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