Editorial: The Recovery College model: state of the art, current research developments and future directions
Bibliographic record
Abstract
The first arAcle, wriWen by the author et al. of this editorial, provides a state-of-the-art review of the studies published since the first studies on the RC model. Briand et al. conducted a comprehensive systemaAc review of RC evaluaAve studies published between 2013 and 2024. Analysis of 64 arAcles revealed five qualitaAve clusters. Early arAcles on the RC focused on implementaAon stages and lessons (2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024). Next, arAcles focused on perceived benefits, learners' experience and acAve ingredients (2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024). ArAcles then moved on to outcomes evaluaAon (2015-2024) and service uAlizaAon and costs (2019)(2020)(2021)(2022)(2023)(2024). Finally, arAcles focused on documenAng an internaAonal scope of the RC and providing a status report and global mulAcenter comparisons (2019)(2020)(2021)(2022)(2023). These qualitaAve clusters capture the scope and richness of the studies, but also the progression in study quality over the past 10 years. To keep pace with this progression, future studies need to consolidate outcome measurement, increase internaAonal and mulAcenter studies, and more systemaAcally measure the quality of implementaAon and the support needed for trainers to ensure this quality. The arAcles presented in this Research Topic provide some answers to these challenges. The arAcles can be grouped into three themaAc groups: (1) understanding learning frame, (2) implementaAon recommendaAons, (3) measuring outcomes.The first group of arAcles focuses on understanding how the RC learning frame works and how it drives change. RCs offer a unique social space that requires the embodiment of values through concrete principles and operaAons. This learning space is complex and fragile. The three arAcles in this group discuss this topic in great depth, providing an even beWer understanding of the RC model. are implemented and how parAcipants experience such value-driven pracAce. The results highlight how RCs facilitate opportuniAes for recovery by fostering spaces for dialogue and co-creaAon, while revealing the fragility and the complexity of these spaces. Understanding their value requires examining how and when these spaces emerge or become constrained, as well as the factors that influence these dynamics.The second group of arAcles examines the condiAons favorable to implementaAon and how we can beJer meet the needs of learners and beJer support and engage trainers. The complex implementaAon of the RC model requires conAnuous quesAoning of how to respect its core values and principles, adjust to its environment and needs, operate in an integrated way within the system and achieve its goals (Parsons' social acAon model). The four arAcles in this group provide a sAmulaAng starAng point for further reflecAon and development: course content selecAon, involvement of learners in course co-producAon, and beWer support for trainers. The third group of arAcles focuses on measuring and understanding outcomes. In recent years, RC courses have addressed the needs of a wide variety of learners (youth, seniors, homeless people, health and educaAonal professionals, etc.). Measuring outcomes must be able to account for the specific effects on these diverse clienteles. The two arAcles of this group suggest new methodological avenues for future research.• Alam et al. conducted a scoping review of potenAal outcome measures to assess the impact of RC courses on demenAa. The lack of validated outcome measures in this context makes it difficult to evaluate the effecAveness of RC courses. Fourteen instruments related to hope, resilience, self-efficacy, empowerment, and adaptaAon were idenAfied. However, the authors called for the development of more context-sensiAve, relaAonal, and recovery-oriented tools tailored to these specific populaAons. The authors who contributed to this Research Topic reached insighiul conclusions, which contributed to the expansion of knowledge regarding the state of the art of RC research. As highlighted in the systemaAc review of Briand and colleagues, the field is progressing toward greater methodological rigor. We must conAnue in this direcAon.Five future direcAons emerge clearly:1. Strengthening outcome research with larger, more robust designs, long-term follow-up, and rigorous evaluaAon frameworks.2. Expanding mulAcentric and internaAonal studies to reflect diverse sejngs and cultural dynamics.3. Assessing outcomes and model fidelity in specific populaAons and contexts, including underrepresented groups such as LGBTQ+ individuals, vulnerable groups, older adults, and people living with cogniAve impairments.4. Clarifying and protecAng model fidelity, while allowing for flexible, locally grounded adaptaAons that preserve RC values.Embedding RCs into broader mental health strategies, including the delivery of training programs, sAgma reducAon, and community-based innovaAon.Recovery Colleges have demonstrated the potenAal to foster personal empowerment, systemic change, and inclusive ciAzenship. Fully realizing this potenAal will require research that is both methodologically rigorous and grounded in lived experience-research that pays aWenAon to context, egalitarianism, and the voices of those most ohen excluded. This Research Topic invites conAnued collaboraAon across disciplines, contexts, and countries.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".