Predictors of long-term prognosis of depression
Bibliographic record
Abstract
BACKGROUND: Many people with depression experience repeated episodes. Previous research into the predictors of chronic depression has focused primarily on the clinical features of the disease; however, little is known about the broader spectrum of sociodemographic and health factors inherent in its development. Our aim was to identify factors associated with a long-term negative prognosis of depression. METHODS: We included 585 people aged 16 years and older who participated in the 2000/01 cycle of the National Population Health Survey and who reported experiencing a major depressive episode in 2000/01. The primary outcome was the course of depression until 2006/07. We grouped individuals into trajectories of depression using growth trajectory models. We included demographic, mental and physical health factors as predictors in the multivariable regression model to compare people with different trajectories. RESULTS: Participants fell into two main depression trajectories: those whose depression resolved and did not recur (44.7%) and those who experienced repeated episodes (55.3%). In the multivariable model, daily smoking (OR 2.68, 95% CI 1.54-4.67), low mastery (i.e., feeling that life circumstances are beyond one's control) (OR 1.10, 95% CI 1.03-1.18) and history of depression (OR 3.5, 95% CI 1.95-6.27) were significant predictors (p < 0.05) of repeated episodes of depression. INTERPRETATION: People with major depression who were current smokers or had low levels of mastery were at an increased risk of repeated episodes of depression. Future studies are needed to confirm the predictive value of these variables and to evaluate their accuracy for diagnosis and as a guide to treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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 teacher head, 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".