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Record W1807672739 · doi:10.1002/ajim.22414

Gender differences in occupational injury incidence

2015· article· en· W1807672739 on OpenAlexaff
Janneke Berecki‐Gisolf, Peter Smith, Alex Collie, Rod McClure

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & HealthPublic Health OntarioUniversity of Toronto
FundersWorkSafe VictoriaInstitute for Safety, Compensation and Recovery ResearchTransport Accident Commission
KeywordsMedicineIncidence (geometry)Occupational exposureOccupational safety and healthOccupational medicineEpidemiologyEnvironmental healthPhysical therapyInternal medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe the frequency and distribution of workplace injury claims by gender, and quantify the extent to which observed gender differences in injury claim rates are attributable to differential exposure to work-related factors. METHODS: WorkSafe Victoria (Australia) workers' compensation data (254,704 claims with affliction onset 2004-2011) were analysed. Claim rates were calculated by combining compensation data with state-wide employment data. RESULTS: Mental disorder claim rates were 1.9 times higher among women; physical injury claim rates were 1.4 times higher among men. Adjusting for occupational group reversed the gender difference in musculoskeletal and tendon injury claim rates, i.e., these were more common in women than men after adjusting for occupational exposure. CONCLUSIONS: Men had higher rates of physical injury claims than women, but this was mostly attributable to occupational factors. Women had higher rates of mental disorder claims than men; this was not fully explained by industry or occupation.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.087
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.389
GPT teacher head0.530
Teacher spread0.141 · 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

Citations57
Published2015
Admission routes1
Has abstractyes

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