Pattern of Change of Depressive Disorder over a One-Year Period among Community-Dwelling Older Adults in Québec
Bibliographic record
Abstract
The objective of this study was to describe changes in depression and its correlates, in community-dwelling elderly, over a 12-month period. Data come from a longitudinal ESA Study (Enquête sur la Santé des Aînés) of elderly persons (n = 2752). Depression was measured using the DSM-IV criteria. Polytomous logistic regression was used to assess relations, over time, between participant's characteristics and depression. Among the 164 (5.9%) subjects, who were depressed at baseline, 19.5% were continuously ill cases and 80.4% had recovered, 12 months later. In polytomous regression, factors increasing the probability of the three depression states (persistence, recovery, and incidence) were daily hassles, stress intensity, and fair/poor self-rated mental health. Depression in old age is dynamic. Available prognostic factors can be taken into account to help direct treatment to elderly at highest risk of a poor prognosis.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".