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Record W4405616904 · doi:10.1177/10482911241298948

A Content Analysis of Web-Based Heat Stress Materials Published by Occupational Health and Safety Ministries, Associations, and Agencies in Canada

2024· article· en· W4405616904 on OpenAlexafffundabout
Emily J. Tetzlaff, Brodie J. Richards, Katie E. Wagar, Roberto C. Harris‐Mostert, W. Shane Journeay, Fergus K. O’Connor, Glen P. Kenny

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of TorontoProvidence Health CareDalhousie UniversityUniversity of Ottawa
FundersWorkplace Safety and Insurance Board
KeywordsHeat stressContent analysisChristian ministryOccupational safety and healthAgency (philosophy)Control (management)Environmental healthForensic engineeringBusinessEngineeringPsychologyMedicinePolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

An ever-increasing number of workplaces are becoming heat-exposed due to rising temperature extremes. However, a comprehensive review of Canadian safety materials available to support workplaces in managing this critical hazard has not previously been conducted. We undertook a review and a content analysis of heat stress materials on safety-based ministry, association, and agency websites in Canada (n = 155) to identify content related to heat stress (n = 595). Each document was qualitatively analyzed using NVivo. The most dominant components identified were heat stress control measures (n = 492, 83%), training and education (n = 414, 70%), workplaces and workers at risk (n = 361, 61%), exposure limits and monitoring practices (n = 344, 58%), and emergency response and reporting (n = 249, 42%). However, the content within these programming components was highly variable. While we found that organizations across Canada provide heat stress content, there was evidence of inconsistencies and considerable gaps in the availability of material and the strategies presented to control the critical risk posed by heat.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.068
GPT teacher head0.333
Teacher spread0.265 · 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 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

Citations7
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicClimate Change and Health ImpactsFrench-language works237,207