Supporting Recovery College trainers: a qualitative study on complementary knowledge in Quebec and Lombardy
Bibliographic record
Abstract
Background: Recovery Colleges (RCs) are educational centers offering free courses on mental health, well-being, recovery, and living well together. They represent an innovative approach to mental health, going beyond clinical and therapeutic interventions to foster constructive dialogue between people with lived experience and professionals with theoretical or clinical knowledge. Fidelity to the RC model, particularly to the principle of co-production, is considered essential to ensure quality. However, despite the crucial role of trainers for maintaining alignment with RC principles and values, little research has examined how trainers could be trained and supported to coproduce RC courses. This study aimed to explore the experiences of RC trainers and coordinators, describing challenges and good practices encountered in working with complementary types of knowledge. Methods: A qualitative exploratory multicenter design was adopted. Data were collected between May and December 2024 through five online focus groups involving trainers and coordinators from two RCs, one in Quebec, Canada, and one in Lombardy, Italy. Verbatim transcripts were analyzed using a stepwise thematic analysis. Results: Twenty-seven people with diverse backgrounds participated in the study. Eight main themes (and their respective subthemes) emerged from participants' narratives: the distinctive nature of the RC model which requires the embodiment of its values; the development of core competencies such as knowledge integration, mobilization of experiential knowledge, and group facilitation skills; the dynamic within the trainers' dyads, described as a relational process based on mutual trust and negotiation; strengths and challenges of the co-production process within the dyad and with learners; ongoing activities and tools to ensure trainers' alignment with the model and activities to support the trainer's role. Discussion: Results suggest the importance of raising awareness among trainers about relevant elements to be considered in designing and implementing a RC training program. It is therefore important to foster egalitarian and supportive relationships in the trainers' dyad, as these can serve as a model for co-production during RC courses. Finally, to improve knowledge complementarity, trainers need to receive continuous support, through ongoing training activities and other learning opportunities to ensure alignment with value and principles of RC model.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".