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Record W2905182826 · doi:10.1111/1753-6405.12859

Men’s work, women’s work and suicide: a retrospective mortality study in Australia

2018· article· en· W2905182826 on OpenAlexaff
Allison Milner, Tania King

Bibliographic record

VenueAustralian and New Zealand Journal of Public Health · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsRetrospective cohort studyOccupational safety and healthWork (physics)Suicide preventionHuman factors and ergonomicsMedicineInjury preventionPoison controlMedical emergencyDemographyGerontologySociologyEngineeringSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: This research sought to investigate the influence of being a in male-dominated occupation on suicide. METHODS: A population-level retrospective mortality study was conducted over the period 2001 to 2015. Data from the Australian Census and the National Coronial Information System were combined. Negative binomial regression was used to assess the relationship between occupational gender ratio and suicide rates, controlling for age, socioeconomic status and year of death. Probabilistic sensitivity analysis accounted for unmeasured confounding due to common mental disorders. RESULTS: Males in male-dominated occupations had a rate ratio (RR) of 7.50 (95%CI 6.07 to 9.25) compared to males in female-dominated occupations. Females in male-dominated occupations had a RR of 0.13 (95%CI 0.07 to 0.26) compared to females in female-dominated occupations. Results for males were maintained after adjusting for common mental disorders. There was evidence of interaction on both additive and multiplicative scales. CONCLUSIONS: The gendered context of an occupation influences suicide, with varying risks for women and men. More research is needed to understand the mechanisms of this relationship. Implications for public health: These results suggest the need for targeted suicide prevention activities in male-dominated occupational groups.

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)0.000
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 score0.083
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.174
GPT teacher head0.412
Teacher spread0.238 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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