Predictors of 1-year outcomes of major depressive disorder among individuals with a lifetime diagnosis: a population-based study
Bibliographic record
Abstract
BACKGROUND: Examining predictors of the outcomes of major depressive disorder (MDD) is important for clinical practice and population health. There are few population-based longitudinal studies on this topic. The objectives of this study were to (1) estimate the proportions of persistent and recurrent MDD among those with MDD over 1 year, and (2) identify demographic, socio-economic, workplace psychosocial and clinical factors associated with the outcomes. METHOD: From a population-based longitudinal study of the working population, participants with a lifetime diagnosis of MDD were selected (n=834). They were classified into two groups: those with and those without current MDD. The proportions of 1-year persistence and recurrence of MDD were estimated. MDD was assessed by the World Health Organization (WHO) Composite International Diagnostic Interview, CIDI-Auto 2.1, by telephone. RESULTS: The proportions of persistent and recurrent MDD in 1 year were 38.5% [95% confidence interval (CI) 31.1-46.5] and 13.3% (95% CI 10.2-17.1) respectively. Long working hours, negative thinking and having co-morbid social phobia were predictive of persistence of MDD. Perceived work-family conflict, the severity of a major depressive episode and symptoms of depressed mood were significantly associated with the recurrence of MDD. CONCLUSIONS: Clinical and psychosocial factors are important in the prognosis of MDD. The factors associated with persistence and recurrence of MDD may be different. More large longitudinal studies on this topic are needed so that clinicians may predict potential outcomes based on the clinical profile and provide interventions accordingly. They may also take clinical action to change relevant psychosocial factors to minimize the chance of persistence and/or recurrence of MDD.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".