Prospective Evaluation of the Effect of Major Depression on Working Status in a Population Sample
Bibliographic record
Abstract
OBJECTIVE: Numerous surveys have reported associations between major depressive episodes (MDEs) and occupational status, but cross-sectional studies cannot quantify the risks of employment transitions nor clarify their temporal direction. The goal of our study was to estimate the impact of MDE on subsequent employment status in a longitudinal community cohort. METHODS: Data from the National Population Health Survey (NPHS) were used. Proportional hazard models and logistic regression were employed to evaluate the effect of MDE on working status during the 1994 to 2004 interval among respondents who reported working at a job or business at baseline. RESULTS: MDE was associated with an increased risk of movement to nonworking status. People aged 26 to 45 years with MDEs have more than double the risk of this transition (HR = 2.6; 95% CI 1.8 to 3.6, P < 0.001). The probability of transition to nonworking status was higher, but the relative effect was smaller in people aged 46 to 65 years (HR = 1.2; 95% CI 0.7 to 2.0, P = 0.47). Retirement or perceived lack of availability of work did not contribute to the association. CONCLUSIONS: MDE is associated with an elevated risk of transition from working to nonworking status, especially in people aged 26 to 45 years.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".