Controlled clinical trial of a self-management program for people with mental illness in an adult mental health service – the Optimal Health Program (OHP)
Bibliographic record
Abstract
Objective. The objective of this study was to evaluate the effect and cost-effectiveness of a self-management intervention, delivered as part of routine care in an adult mental health service. Method. In a community mental health setting, routine care was compared with routine care plus a nine-session intervention (the Optimal Health Program) using a non-randomised controlled design. Adult (18–65 years) consumers of mental health services in the Australian Capital Territory were eligible for participation. Results. The Optimal Health Program was associated with significant improvements in health and social functioning as measured by the Health of the Nation Outcome Scale (average change relative to control: –3.17; 95% CI –4.49 to –1.84; P < 0.001). In addition, there was a reduction in hospital admissions in the treatment group (percentage of time in hospital reduced from 3.20 to 0.82; P = 0.07). This translated into a net cost saving of over AU$6000 per participant per year (uncertainty range AU$744 to AU$12 656). Conclusions. This study shows promising results for incorporating a self-management program into routine care to improve the health and social functioning of mental health consumers in a cost-effective manner. What is known about the topic? Current literature supports the efficacy of structured self-management programs for chronic conditions such as diabetes (type 1 and 2) and asthma, but there remains limited evidence that self-management programs improve outcomes for people with mental illness. What does this paper add? This study adds to the body of evidence supporting self-management as a cost-effective adjunct to routine care in mental health services. What are the implications for practitioners? Our study supports the feasibility of clinicians delivering cost-effective self-management programs as part of routine mental health service delivery.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".