Evidence-based Recovery Colleges: developing a typology based on organisational characteristics, fidelity and funding
Bibliographic record
Abstract
PURPOSE: Recovery Colleges (RCs) have been implemented across England with wide variation in organisational characteristics. The purpose of this study is to describe RCs across England in terms of organisational and student characteristics, fidelity and annual spending, to generate a RC typology based on characteristics and to explore the relationship between characteristics and fidelity. METHODS: All RC in England meeting criteria on recovery orientation, coproduction and adult learning were included. Managers completed a survey capturing characteristics, fidelity and budget. Hierarchical cluster analysis was conducted to identify common groupings and generate an RC typology. RESULTS: Participants comprised 63 (72%) of 88 RC in England. Fidelity scores were high (median 11, IQR 9-13). Both NHS and strengths-focussed RCs were associated with higher fidelity. The median annual budget was £200,000 (IQR £127,000-£300,000) per RC. The median cost per student was £518 (IQR £275-£840), cost per course designed was £5,556 (IQR £3,000-£9,416) and per course run was £1,510 (IQR £682-£3,030). The total annual budget across England for RCs is an estimated £17.6 m including £13.4 m from NHS budgets, with 11,000 courses delivered to 45,500 students. CONCLUSION: Although the majority of RCs had high levels of fidelity, there were sufficiently pronounced differences in other key characteristics to generate a typology of RCs. This typology might prove important for understanding student outcomes and how they are achieved and for commissioning decisions. Staffing and co-producing new courses are key drivers of spending. The estimated budget for RCs was less than 1% of NHS mental health spending.
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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 | 0.092 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".