Supporting Recovery by Improving Patient Engagement in a Forensic Mental Health Hospital
Bibliographic record
Abstract
BACKGROUND: Mental health services are shifting toward approaches that promote patients' choices and acknowledge the value of their lived experiences. OBJECTIVE: To support patients' recovery and improve their experiences of care in a Canadian forensic mental health hospital, an intervention was launched to increase patient engagement by establishing a peer support program, strengthening a patient advisory committee, and creating a patient-led research team. DESIGN: The effect of the intervention on patient- and system-level outcomes was studied using a naturalistic, prospective, longitudinal approach. Quantitative and qualitative data were gathered from inpatients and service providers twice during the 19-month intervention. RESULTS: Despite succeeding in supporting patients' participation, the intervention had minimal impacts on internalized stigma, personal recovery, personal empowerment, service engagement, therapeutic milieu, and the recovery orientation of services. Peer support demonstrated positive effects on internalized stigma and personal recovery. CONCLUSIONS: Strengthening patient engagement contributes toward improving experiences of care in a forensic hospital, but it may have limited effects on outcomes.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".