Recovery Colleges After a Decade of Research: A Literature Review
Bibliographic record
Abstract
OBJECTIVE: Since the first recovery college (RC) opened in England in 2009, many more have begun operating around the world. The body of knowledge regarding the effects of RCs is growing, suggesting their benefit to recovery, well-being, goal achievement, knowledge, self-management, social support, reduced stigma, and service use. The objective of this review was to establish the state of knowledge about RCs from current empirical literature and to document the methods used to evaluate them. METHODS: In consultation with an international expert panel, two independent evaluators performed a literature review with no date limits on publications in the Medline and Scopus electronic databases. RESULTS: A total of 460 articles were found, and 31 publications were retained. RC attendance was associated with high satisfaction among students, attainment of recovery goals, changes in service providers' practice, and reductions in service use and cost. CONCLUSIONS: To our knowledge, this is the first literature review of peer-reviewed publications about original studies evaluating the impacts of RCs, including studies pertaining to students, health service providers' practices, education and management practitioners, and citizens. Quantitative studies with a high level of evidence were underrepresented and should be considered as a future evaluation design. Furthermore, outcomes such as empowerment and reduced stigma should be assessed with standardized tools. The impact of RCs on attendees, family, friends, and caregivers and on the everyday practice of health service providers who attend RCs for continuing education or as tutors should also be assessed.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".