Measuring Depression in Canada's Elderly Chinese Population: Use of a Community Screening Instrument
Bibliographic record
Abstract
OBJECTIVE: To assess the reliability and validity of a 15-item Chinese Geriatric Depression Scale (GDS) to measure depression in Canada's elderly Chinese population. METHOD: A random sample of 96 elderly Chinese in a Canadian city was surveyed by telephone. The measure of depression used was the 15-item Chinese version of the GDS. In addition, the physical and mental health of the respondents was assessed by a Chinese version of the SF-12, questions on self-perceived general health, and questions on self-reported illnesses and health concerns. RESULTS: The prevalence rate of depression in the elderly Chinese who participated in this study is approximately 20%, which is much lower than that of elderly Chinese in the United States (US). Cronbach's alpha and split-half reliability coefficients were 0.88 and 0.89 respectively. GDS scores are significantly correlated with the mental health component (r = -0.74) of the SF-12, indicating a strong convergent validity. GDS scores are also correlated with the physical health component of the SF-12 (r = -0.41), self-perceived general health (r = -0.26), and illnesses (r = 0.52), demonstrating concurrent validity. CONCLUSIONS: The 15-item Chinese GDS has good internal consistency and both convergent and concurrent validity. It can help to identify depression among the elderly Chinese, allowing early identification and prevention of this problem. Further research is required to support its use in clinical settings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 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".