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Enregistrement W4416909342 · doi:10.3389/fpsyt.2025.1745050

Editorial: The Recovery College model: state of the art, current research developments and future directions

2025· editorial· en· W4416909342 sur OpenAlexaff
Catherine Briand, Martine Vallarino, Filippo Rapisarda, Anne Schanche Selbekk

Notice bibliographique

RevueFrontiers in Psychiatry · 2025
Typeeditorial
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensUniversité du Québec à Trois-RivièresInstitut Universitaire en Santé Mentale de QuébecInnovation and Economic Development Trois Rivières
Organismes subventionnairesnon disponible
Mots-clésScope (computer science)PaceQuality (philosophy)Space (punctuation)State (computer science)Frame (networking)

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,033
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,033
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0040,002
Études des sciences et des technologies0,0030,002
Communication savante0,0070,005
Science ouverte0,0030,001
Intégrité de la recherche0,0080,011
Charge utile insuffisante (le modèle a refusé de juger)0,0140,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,055
Tête enseignante GPT0,418
Écart entre enseignants0,363 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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