Treatment satisfaction, perceived treatment effectiveness, and dropout among older users of mental health services
Bibliographic record
Abstract
OBJECTIVES: To examine the rates and correlates of treatment satisfaction, perceived treatment effectiveness, and dropout among older users of mental health services. METHOD: We used data from the Canadian Community Health Survey-Mental Health and Well-Being (CCHS-1.2), which includes 12,792 individuals aged ≥55 years. The average age of these participants was 67 years and 53.2% were female. We examined the rates of treatment satisfaction, perceived treatment effectiveness, and dropout for those who had used mental health services in the past year, and used logistic regression to examine the correlates of these outcomes. RESULTS: Of the older adults included in the CCHS-1.2, 664 (5.3%) had used mental health services in the past year. The majority of these were satisfied with services (88.5%) and perceived treatment to be effective (83.6%), which is likely why only 15.5% dropped out in the past year. In logistic regression models, social support was significantly and positively related to both treatment satisfaction and perceived effectiveness. Perceived treatment effectiveness was the only variable related to dropout, with lower levels of perceived effectiveness associated with greater odds of dropping out of treatment. CONCLUSIONS: Results from this study indicate that older adults have very good self-reported treatment outcomes. The modest influence of individual characteristics on treatment outcomes suggests the potential importance of contextual characteristics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".