Prevalence of Mental Disorders among Older Chinese People in Tianjin City
Bibliographic record
Abstract
OBJECTIVE: Population aging is accelerating across the world, and older people have a higher risk of mental disorders. Most studies focus on one mental disorder, and only report the current prevalence. Besides, these studies use screening scales for symptoms of mental disorders, which may induce biased results. In this study, we used data for diagnoses based on SCID that had been administered by trained psychiatrists to explore the 1-month and lifetime prevalence of mental disorders among a Chinese aged cohort. METHODS: Data for this study was derived from the Tianjin Mental Health Survey. Participants were first screened using a General Health Questionnaire and 9 additional items on other risk factors for mental disorders, and then diagnosed with the Chinese version of Structured Clinical Interview for Diagnostic and Statistical Manual (DSM-IV) Axis I disorders. A total of 3,325 people aged 60 and above had valid information, and 1,486 completed the SCID interview. RESULTS: The weighted 1-month prevalence of mental disorders was 14.27%, whereas the lifetime prevalence of mental disorders was 24.20%. Most of these participants were female, older, currently not married, of lower education level, and with poor family economic status. Organic mental disorders had the highest 1-month prevalence (4.45%), whereas mood disorder was highest for the lifetime prevalence (9.75%). CONCLUSION: Older Chinese people had a high prevalence of mental disorders. Further research and health services innovations are needed to address the high prevalence in these subgroups among older people.
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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 | 0.000 | 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 | 0.000 |
| 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".