Identification of factors associated with depression chronicity: An analysis of the Canadian Study on Aging (CLSA)
Bibliographic record
Abstract
OBJECTIVE: Identification of risk factors for depressive chronicity may reduce disease burden by informing prevention and treatment strategies. We aimed to estimate the prevalence of depression chronicity and explore factors linked to chronic depression in a longitudinal cohort. METHODS: Participants from the Canadian Longitudinal Study on Aging Comprehensive cohort who were depressed at baseline, based on a score of 10 or more on the Center for Epidemiologic Studies Depression-10 item scale (CESD-10), were included (n = 3473). Participants were considered to have chronic depression if they maintained a CESD-10 score of 10 or greater at the follow-up. Latent profile analyses (LPA) were used to determine baseline depression subtypes, baseline allostatic load biomarker (AL) profiles, and adverse childhood experience (ACE) profiles. Logistic regression was used to explore factors associated with chronic depression RESULTS: Depression was chronic for 46.6 % of the sample. Significant baseline predictors included depression subtype, with atypical and melancholic subtypes having 1.35 (95 %CI: 1.05-1.73) and 1.87 (95 %CI: 1.53-2.3) times greater odds, respectively, compared with the positive affect subtype. Greater odds of chronic depression were associated with moderate (OR 1.44, 95 %CI: 1.21-1.72) and physical ACE profiles (OR 1.47, 95 %CI: 1.16, 1.84), and with high-cardiovascular AL profile (OR 1.07, 95 %CI: 1.07-1.69), compared with low ACE and average AL profiles. Other significant baseline risk factors included lower annual household income, increased chronic conditions, lower perceived social status, and smoking. CONCLUSIONS: Depression subtypes and stressor profiles were differentially associated with depression chronicity. The research may shed light on intervention and prevention in clinical practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".