Trajectory and predictors of depressive disorder among community older adults, in Quebéc: A one-year follow-up study
Bibliographic record
Abstract
Introduction Past research has demonstrated the high prevalence of depression in elderly. However, the most of studies followed the symptom trajectory of individuals diagnosed with depression in a clinical setting and few longitudinal studies have characterized the patterns of depression in older adults population-based. Objective To describe changing of depressive disorder in an elderly population-based over a 12-month period and to examine the influence of medical and psychosocial factors on the outcome. Methods Data come from a longitudinal ESA Study (Enquête sur la Santé des Aînés) of elderly community persons (n = 2752). Depression, including major and minor depression, measured using the DSM-IV criteria. Generalized estimating equations (GEE) were used to assess relations between participant characteristics at baseline and depression, 12 months later. Results Among the 164 (5.9%) participants, who are depressed at baseline, 19.5% were continuously ill cases and 80.4% had recovered, 12 months later. Multivariate analyses showed that the risk of depression over the 12-month period was higher among for participants who were separated; living in rural region; with a great number of daily hassles, with high level of stress intensity, great number of chronic disease and with fair/poor perception of mental health. Conclusion Results support the hypothesis about medical and psychosocial factors as predictors over time of depression, in old persons. Using readily available prognostic factors (for example, high level of stress intensity, living in rural region, great number of chronic disease) could help direct treatment to elderly at highest risk of a poor prognosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".