Socioeconomic status and the risk of major depression: the Canadian National Population Health Survey
Bibliographic record
Abstract
BACKGROUND: There are few longitudinal studies investigating the risk of major depression by socioeconomic status (SES). In this study, data from the longitudinal cohort of Canadian National Population Health Survey were used to estimate the risk of major depressive episode (MDE) over 6 years by SES levels. METHODS: The National Population Health Survey used a nationally representative sample of the Canadian general population. In this analysis, participants (n=9589) were followed from 2000/2001 (baseline) to 2006/2007. MDE was assessed using the Composite International Diagnostic Interview--Short Form for Major Depression. RESULTS: Low education level (OR=1.86, 95% CI 1.28 to 2.69) and financial strain (OR=1.65, 95% CI 1.19 to 2.28) were associated with an increased risk of MDE in participants who worked in the past 12 months. In those who did not work in the past 12 months, participants with low education were at a lower risk of MDE (OR=0.43, 95% CI 0.25 to 0.76), compared with those with high education. Financial strain was not associated with MDE in participants who did not work. Working men who reported low household income (12.9%) and participants who did not work and reported low personal income (5.4%) had a higher incidence of MDE than others. CONCLUSIONS: SES inequalities in the risk of MDE exist in the general population. However, the inequalities may depend on measures of SES, sex and employment status. These should be considered in interventions of reducing inequalities in MDE. MDE history is an important factor in studies examining inequalities in MDE.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".