Development and Evaluation of a Recovery College Fidelity Measure
Bibliographic record
Abstract
Objective: Recovery Colleges are widespread, with little empirical research on their key components. This study aimed to characterize key components of Recovery Colleges and to develop and evaluate a developmental checklist and a quantitative fidelity measure. Methods: Key components were identified through a systematized literature review, international expert consultation ( n = 77), and semistructured interviews with Recovery College managers across England ( n = 10). A checklist was developed and refined through semistructured interviews with Recovery College students, trainers, and managers ( n = 44) in 3 sites. A fidelity measure was adapted from the checklist and evaluated with Recovery College managers ( n = 39, 52%), clinicians providing psychoeducational courses ( n = 11), and adult education lecturers ( n = 10). Results: Twelve components were identified, comprising 7 nonmodifiable components (Valuing Equality, Learning, Tailored to the Student, Coproduction of the Recovery College, Social Connectedness, Community Focus, and Commitment to Recovery) and 5 modifiable components (Available to All, Location, Distinctiveness of Course Content, Strengths Based, and Progressive). The checklist has service user student, peer trainer, and manager versions. The fidelity measure meets scaling assumptions and demonstrates adequate internal consistency (0.72), test-retest reliability (0.60), content validity, and discriminant validity. Conclusions: Coproduction and an orientation to adult learning should be the highest priority in developing Recovery Colleges. The creation of the first theory-based empirically evaluated developmental checklist and fidelity measure (both downloadable at researchintorecovery.com/recollect ) for Recovery Colleges will help service users understand what Recovery Colleges offer, will inform decision making by clinicians and commissioners about Recovery Colleges, and will enable formal evaluation of their impact on students.
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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 | 0.006 | 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".