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Record W1936448929 · doi:10.1002/ajim.22535

Individual, occupational, and workplace correlates of occupational health and safety vulnerability in a sample of Canadian workers

2015· article· en· W1936448929 on OpenAlexafffundabout
A. Morgan Lay, Ron Saunders, Marni Lifshen, Curtis Breslin, Anthony D. LaMontagne, Emile Tompa, Peter Smith

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsPublic Health OntarioMcMaster UniversitySeneca PolytechnicUniversity of TorontoInstitute for Work & Health
FundersCanadian Institutes of Health Research
KeywordsMedicineOccupational safety and healthEnvironmental healthSample (material)Occupational medicineOccupational exposureVulnerability (computing)

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe OH&S vulnerability across a diverse sample of Canadian workers. METHODS: A survey was administered to 1,835 workers employed more than 15 hrs/week in workplaces with at least five employees. Adjusted logistic models were fitted for three specific and one overall measure of workplace vulnerability developed based on hazard exposure and access to protective OH&S policies and procedures, awareness of employment rights and responsibilities, and workplace empowerment. RESULTS: More than one third of the sample experienced some OH&S vulnerability. The type and magnitude of vulnerability varied by labor market sub-group. Younger workers and those in smaller workplaces experienced significantly higher odds of multiple types of vulnerability. Temporary workers reported elevated odds of overall, awareness- and empowerment-related vulnerability, while respondents born outside of Canada had significantly higher odds of awareness vulnerability. CONCLUSION: Knowing how labor market sub-groups experience different types of vulnerability can inform better-tailored primary prevention interventions.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.170
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.418
Teacher spread0.284 · 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

Citations45
Published2015
Admission routes3
Has abstractyes

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