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Record W2169567681 · doi:10.2105/ajph.2011.300376

The Impact of Changes in Job Strain and Its Components on the Risk of Depression

2011· article· en· W2169567681 on OpenAlexafffundabout
Peter Smith, Amber Bielecky

Bibliographic record

VenueAmerican Journal of Public Health · 2011
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsInstitute for Work & Health
FundersCanadian Institutes of Health Research
KeywordsJob strainDepression (economics)PsychosocialOdds ratioMarital statusConfidence intervalJob controlMedicineDemographyPopulationCohortPsychiatryPsychologyGerontologyWork (physics)Environmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We assessed the impact of changes in dimensions of the psychosocial work environment on risk of depression in a longitudinal cohort of Canadian workers who were free of depression when work conditions were initially reported. METHODS: Using a sample (n = 3735) from the Canadian National Population Health Survey, we examined the effects of changes in job control, psychological demands, and social support over a 2-year period on subsequent depression. We adjusted models for a number of covariates, including personal history of depression. RESULTS: Respondents with increased psychological demands were more likely to have depression over the following 2 years (odds ratio = 2.36; 95% confidence interval = 1.14, 4.88). This risk remained statistically significant after adjustment for age, gender, marital status, presence of children, level of education, chronic health conditions, subclinical depression when work conditions were initially assessed, family history of depression, and personal history of depression. CONCLUSIONS: These results demonstrate that changes in psychological demands have a stronger influence than changes in job control on the onset of depression, highlighting the importance of not assuming an interaction between these 2 components of job strain when assessing health outcomes.

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)0.010
metaresearch head score (Gemma)0.001
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 score0.147
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.415
Teacher spread0.306 · 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

Citations50
Published2011
Admission routes3
Has abstractyes

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