Community study of depression in old age in Taiwan
Bibliographic record
Abstract
BACKGROUND: Published studies of prevalence of depression in old age in Taiwan have yielded equivocal results. AIMS: To study the prevalence of depressive disorders among community-dwelling elderly; further, to assess socio-demographic correlates and life events in relation to depression. METHOD: A randomised sample of 1500 subjects aged 65 and over was selected from three communities. Research psychiatrists conducted all assessments using the Geriatric Mental State Schedule. The diagnosis of depression was made with the GMS-AGECAT (Automated Geriatric Examination for Computerised Assisted Taxonomy); data on life events were collected with the Taiwanese version of the Life Events and Difficulties Schedule. RESULTS: One-month prevalence of psychiatric disorders was 37.7%, with 15.3% depressive neurosis and 5.9% major depression. A high risk of depressive disorders was found among widows with a low educational level living in the urban community, and among those with physical illnesses. CONCLUSIONS: Contrary to most previous reports, we found that the prevalence of depressive disorders among the elderly in the community in Taiwan is high and comparable to rates reported in some studies of UK samples.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".