Recovery college features and context: advancing a recovery and well-being policy agenda
Bibliographic record
Abstract
Purpose Internationally, there has been a move towards more recovery-oriented mental health policies for people living with mental illness, and some countries have included well-being as a population-level objective. In practice, these policy objectives can be difficult to achieve because of deeply rooted policy legacies, including a biomedical approach to care and the stigma associated with mental illness. The purpose of this paper is to investigate how interventions that operate outside the formal mental health system, such as recovery colleges (RCs), may advance these policy objectives more easily than efforts at broader system reform. Design/methodology/approach This study conducted a scoping review to explore the features and context of RCs that make the model an attractive and feasible opportunity to advance a recovery and well-being agenda. Our research is motivated by the initial and growing adoption of RCs by the Canadian Mental Health Association. This paper applies the consolidated framework for implementation research to analyse features of the model and the context of its implementation in Canada. Findings The RC’s educational approach, adaptability, coproduced nature and positioning outside the formal mental health system are key features that facilitate implementation without disrupting deeply entrenched policy legacies. Other facilitators in the Canadian context include the implementing organisation’s independence from government, its federated structure and the model’s alignment with national policy objectives. Originality/value This paper highlights how interventions outside the formal mental healthcare system can promote stated recovery and well-being policy goals.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".