“We’re all learning together”: exploring peer educator engagement in Recovery Colleges through a participatory research approach
Bibliographic record
Abstract
Introduction and purpose: Recovery Colleges offer a community-based, recovery-oriented approach that promotes mental health and personal growth through co-produced, peer-led courses. Despite their growth in Canada, limited research examines factors influencing peer educators' sustained engagement-an essential aspect of program sustainability. This study addresses this gap by identifying key factors and developing best practices to support peer educators in Recovery Colleges and enhance retention and well-being. Methods: This study employed a mixed-methods Participatory Action Research (PAR) approach, engaging peer educators as co-researchers. A Committee of seven local peer educators (five remained actively involved) co-designed tools and interpreted findings as the advisory peer educators. All Canadian Recovery Colleges were invited to participate. Data were collected from peer educators and program organizers via an online survey and virtual interviews (n=32, across nine provinces). Qualitative data were analyzed using thematic analysis, with coding refined through an iterative process. Results: We identified five themes for sustaining peer educator engagement: Inclusivity, Connectedness, Adaptability, Empowerment, and Implementation Factors. Practical recommendations emerged for recruitment, training, and workplace support. The findings emphasize the need for inclusive, adaptable, and empowering environments to sustain peer educator engagement in Recovery Colleges. Discussion: Centring peer educator experiences is critical to upholding Recovery Colleges' values and creating inclusive, meaningful learning environments that promote personal and community growth. The participatory nature of the research highlighted the unique insights of our advisory peer educators and echoed the Recovery College principles of promoting recovery and building on individual strengths.
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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 | 0.090 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".