Cultural influences on fidelity components in recovery colleges: a study across 28 countries and territories
Bibliographic record
Abstract
Background: Recovery colleges (RCs) support personal recovery through education, skill development and social support for people with mental health problems, carers and staff. Guided by co-production and adult learning principles, RCs represent a recent mental health innovation. Since the first RC opened in England in 2009, RCs have expanded to 28 countries and territories. However, most RC research has been conducted in Western countries with similar cultural characteristics, limiting understanding of how RCs can be culturally adapted. The 12-item Recovery Colleges Characterisation and Testing (RECOLLECT) Fidelity Measure (RFM) evaluates the operational fidelity of RCs based on 12 components, but cultural influences on these components remain underexplored. Aims: To assess associations between Hofstede's cultural dimensions and RFM items to identify cultural influences on fidelity components. Methods: A cross-sectional survey of RC managers was conducted across all 221 RCs. Mixed-effects regression models examined associations between Hofstede's country-level cultural dimensions and item-level RFM scores, adjusted for healthcare expenditure and income inequality. Four cultural dimensions, obtained from Hofstede, were analysed: individualism (prioritising personal needs), indulgence (enjoyment-oriented), uncertainty avoidance (preference for predictability) and long-term orientation (future-focused). Results: The RFM was completed by 169 (76%) RC managers. Seven RFM items showed associations with cultural dimensions. Equality was linked to short-term orientation, while learning was associated with individualism and uncertainty avoidance. Both individualism and indulgence influenced co-production and community focus. Commitment to recovery was shaped by all four cultural dimensions, with the strongest associations seen for individualism and indulgence. Individualism enhanced explicit focus on strengths-based practice, while uncertainty avoidance influenced course distinctiveness. Conclusions: This study demonstrates how culture shapes RC fidelity components, providing actionable insights for cultural adaptation. Incorporating under-represented dimensions, such as collectivism and restraint, could improve the RFM's global applicability, facilitating implementation. Future research should explore cultural nuances, engage diverse stakeholders and refine fidelity measures to enhance RC inclusivity and effectiveness worldwide.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".