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Record W2102641334 · doi:10.1080/14034940601048091

The impact of psychosocial work conditions on attempted and completed suicide among western Canadian sawmill workers

2007· article· en· W2102641334 on OpenAlexafffundabout
Aleck Ostry, Stefania Maggi, James Tansey, James R. Dunn, Ruth Hershler, Lisa Chen, Amber Louie, Clyde Hertzman

Bibliographic record

VenueScandinavian Journal of Public Health · 2007
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsThompson Rivers UniversityUniversity of British Columbia
FundersHealth CanadaMichael Smith Health Research BC
KeywordsPsychosocialMarital statusJob controlPoison controlMedicineLogistic regressionOddsOccupational safety and healthOdds ratioMultivariate analysisCohortDemographyInjury preventionSuicide preventionGerontologyEnvironmental healthPsychiatryPopulationWork (physics)

Abstract

fetched live from OpenAlex

BACKGROUND: Using a large cohort of western Canadian sawmill workers (n = 28,794), the association between psychosocial work conditions and attempted and completed suicide was investigated. METHODS: Records of attempted and completed suicide were accessed through a provincial hospital discharge registry to identify cases that were then matched using a nested case control method. Psychosocial work conditions were estimated by expert raters using the demand-control model. Univariate and multivariate conditional logistic regression was used to estimate the association between work conditions and suicide. RESULTS: In multivariate models, controlling for sociodemographic (marital status, ethnicity) and occupational confounders (job mobility and duration), low psychological demand was associated with increased odds for completed suicide, and low social support was associated with increased odds for attempted suicides. CONCLUSIONS: This study indicates that workers with poor psychosocial working conditions may be at increased risk of both attempted and completed suicide.

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)0.003
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 score0.031
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.087
GPT teacher head0.405
Teacher spread0.318 · 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
Published2007
Admission routes3
Has abstractyes

Explore more

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