Major Depressive Episodes and Work Stress: Results From a National Population Survey
Bibliographic record
Abstract
OBJECTIVES: We determined the proportion of workers meeting criteria for major depressive episodes in the past year and examined the association between psychosocial work-stress variables and these episodes. METHODS: Data were derived from the Canadian Community Health Survey 1.2, a population-based survey of 24324 employed, community-dwelling individuals conducted in 2002. We assessed depressive episodes using the Composite International Diagnostic Interview. RESULTS: Of the original sample, 4.6% (weighted n=745948) met criteria for major depressive episodes. High job strain was significantly associated with depression among men (odds ratio [OR]=2.38; 95% confidence interval [CI]=1.29, 4.37), and lack of social support at work was significantly associated with depression in both genders (men, OR=2.70; 95% CI=1.55, 4.71; women, OR=2.37; 95% CI=1.71, 3.29). Women with low levels of decision authority were more likely to have depression (OR=1.59; 95% CI=1.06, 2.39) than were women with high levels of authority. CONCLUSIONS: A significant proportion of the workforce experienced major depressive episodes in the year preceding our study. Gender differences appear to affect work-stress factors that increase risk for depression. Prevention strategies need to be developed with employers and employee organizations to address work organization and to increase social support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".