Mental Health Reform at a Systems Level: Widening the Lens on Recovery-Oriented Care
Bibliographic record
Abstract
This paper is an initial attempt to collate the literature on psychiatric inpatient recovery-based care and, more broadly, to situate the inpatient care sector within a mental health reform dialogue that, to date, has focused almost exclusively on outpatient and community practices. We make the argument that until an evidence base is developed for recovery-oriented practices on hospital wards, the effort to advance recovery-oriented systems will stagnate. Our scoping review was conducted in line with the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (commonly referred to as PRISMA) guidelines. Among the 27 papers selected for review, most were descriptive or uncontrolled outcome studies. Studies addressing strategies for improving care quality provide some modest evidence for reflective dialogue with former inpatient clients, role play and mentorship, and pairing general training in recovery oriented care with training in specific interventions, such as Illness Management and Recovery. Relative to some other fields of medicine, evidence surrounding the question of recovery-oriented care on psychiatric wards and how it may be implemented is underdeveloped. Attention to mental health reform in hospitals is critical to the emergence of recovery-oriented systems of care and the realization of the mandate set forward in the Mental Health Strategy for Canada.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".