Finding connection “while everything is going to crap”: experiences in Recovery Colleges during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Recovery Colleges (RCs) are mental health and well-being education centres where people come together and learn skills that support their wellness. Co-production, co-learning and transformative education are fundamental to RCs. People with lived experience are recognized as experts who partner with health professionals in the design and actualization of educational programming. The pandemic has changed how RCs operate by necessitating a shift from in-person to virtual offerings. Given the relational ethos of RCs, it is important to explore how the experiences of RC members and communities were impacted during this time. To date, there has been limited scholarship on this topic. Methods In this exploratory phase of a larger project, we used participatory action research to interview people who were accessing, volunteering and/or working in RCs across Canada. Semi-structured interviews were conducted with twenty-nine individuals who provided insights on what is important to them about RC programming. Results Our study was conducted amid the COVID-19 pandemic. Accordingly, participants elucidated how their involvement in RCs was impacted by pandemic related restrictions. The results of this study demonstrate that RC programming is most effective when it: (1) is inclusive; (2) has a “good vibe”; and (3) equips people to live a fuller life. Conclusions The pandemic, despite its challenges, has yielded insights into a possible evolution of the RC model that transcends the pandemic-context. In a time of great uncertainty, RCs served as safe spaces where people could redefine, pursue, maintain or recover wellness on their own terms.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".