Organisational and student characteristics, fidelity, funding models, and unit costs of recovery colleges in 28 countries: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Recovery colleges were developed in England to support the recovery of individuals who have mental health symptoms or mental illness. They have been founded in many countries but there has been little international research on recovery colleges and no studies investigating their staffing, fidelity, or costs. We aimed to characterise recovery colleges internationally, to understand organisational and student characteristics, fidelity, and budget. METHODS: In this cross-sectional study, we identified all countries in which recovery colleges exist. We repeated a cross-sectional survey done in England for recovery colleges in 28 countries. In both surveys, recovery colleges were defined as services that supported personal recovery, that were coproduced with students and staff, and where students learned collaboratively with trainers. Recovery college managers completed the survey. The survey included questions about organisational and student characteristics, fidelity to the RECOLLECT Fidelity Measure, funding models, and unit costs. Recovery colleges were grouped by country and continent and presented descriptively. We used regression models to explore continental differences in fidelity, using England as the reference group. FINDINGS: We identified 221 recovery colleges operating across 28 countries, in five continents. Overall, 174 (79%) of 221 recovery colleges participated. Most recovery colleges scored highly on fidelity. Overall scores for fidelity (β=-2·88, 95% CI 4·44 to -1·32; p=0·0001), coproduction (odds ratio [OR] 0·10, 95% CI 0·03 to 0·33; p<0·0001), and being tailored to the student (OR 0·10, 0·02 to 0·39; p=0·0010), were lower for recovery colleges in Asia than in England. No other significant differences were identified between recovery colleges in England, and those in other continents where recovery colleges were present. 133 recovery colleges provided data on annual budgets, which ranged from €0 to €2 550 000, varying extensively within and between continents. From included data, all annual budgets reported by the college added up to €30 million, providing 19 864 courses for 55 161 students. INTERPRETATION: Recovery colleges exist in many countries. There is an international consensus on key operating principles, especially equality and a commitment to recovery, and most recovery colleges achieve moderate to high fidelity to the original model, irrespective of the income band of their country. Cultural differences need to be considered in assessing coproduction and approaches to individualising support. FUNDING: National Institute for Health and Care Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".