Prevalence of mental disorders and service utilization in seniors: results from the Canadian community health survey cycle 1.2.
Bibliographic record
Abstract
OBJECTIVE: This paper examined the 12-month rate of mental disorders and subsequent service use among Canadian seniors aged 65-79 and 80 and over and adults aged 20-49 and 50-64. METHODS: This is a secondary analysis of data [n = 33,695] of the population-based Canadian Community Health survey, Cycle 1.2 (CCHS 1.2). Major Depressive Episodes (MDEs), specific Anxiety Disorders (AD) and Alcohol Abuse/Dependence rates were assessed. RESULTS: The 12-month rates for the two senior groups aged 65-79 and 80+ for MDEs, ADs, and Substance Dependence (SD) are 1.7% and 1.6%; 1.4% and 0.8%; and 0.1% and 0.0%, respectively. Seniors aged 65-79 and 80+ had significantly lower rates than adults aged 20-49 and 50-64 for all mental disorders and SD. Among the total sample, seniors aged 65-79 and 80+ had significantly lower utilization rates than adults aged 20-49 and 50-64 for all three provider categories (GP only, GP and Other Health Professional, Any Other Health Professional but GP). Females aged 65-79 and 80+ were less likely to have reported service use for mental health reasons as opposed to those females aged 20-49 and 50-64. Males aged 65-79 and 80+ were more likely to have reported having used mental health services in the past 12 months as opposed to males aged 20-49 and 50-64. CONCLUSIONS: The presence of common mental disorders is lower in seniors as compared to younger adults. Age and gender influence mental health service utilization rates. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".