Participants’ Evaluation and Outcomes following Integration of Self-Management Support into Outpatient Schizophrenia Case Management
Bibliographic record
Abstract
(1) Background: Self-management is advocated as a feasible, effective intervention to support individuals to actively manage the impact of illness and live healthier lives. We sought to evaluate a piloted self-management model, SET for Health, tailored for individuals living with schizophrenia embedded within ambulatory case management. (2) Methods: A mixed-methods design engaged 40 adults living with schizophrenia in the SET for Health protocol. Functional and symptomatic outcomes were measured by self-report and clinician ratings at baseline and completion of self-management plans, on average one year later. Semi-structured qualitative client interviews invited evaluations of personal experiences with the intervention. (3) Results: Significant improvements were found concerning client illness severity, social and occupational functioning, illness management and functional recovery with reductions in emergency visits and days in hospital. Clients endorsed the value of the intervention. Baseline clinical characteristics did not predict who benefited. Participation contributed to motivational gains and quality of life. (4) Conclusions: Results confirmed self-management support embedded within traditional case management improved clients' clinical and functional status, and contributed to quality of life. Clients engaged in their recovery and actively used self-management strategies. Self-management can be successfully adopted by clients with schizophrenia regardless of age, gender, education, illness severity or duration.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".