Development and Evaluation of a Recovery College Fidelity Measure.

OBJECTIVE: Recovery Colleges are widespread, with little empirical research on their key components. This study aimed to characterize key components of Recovery Colleges and to develop and evaluate a developmental checklist and a quantitative fidelity measure. METHODS: Key components were identified through a systematized literature review, international expert consultation (n = 77), and semistructured interviews with Recovery College managers across England (n = 10). A checklist was developed and refined through semistructured interviews with Recovery College students, trainers, and managers (n = 44) in 3 sites. A fidelity measure was adapted from the checklist and evaluated with Recovery College managers (n = 39, 52%), clinicians providing psychoeducational courses (n = 11), and adult education lecturers (n = 10). RESULTS: Twelve components were identified, comprising 7 nonmodifiable components (Valuing Equality, Learning, Tailored to the Student, Coproduction of the Recovery College, Social Connectedness, Community Focus, and Commitment to Recovery) and 5 modifiable components (Available to All, Location, Distinctiveness of Course Content, Strengths Based, and Progressive). The checklist has service user student, peer trainer, and manager versions. The fidelity measure meets scaling assumptions and demonstrates adequate internal consistency (0.72), test-retest reliability (0.60), content validity, and discriminant validity. CONCLUSIONS: Coproduction and an orientation to adult learning should be the highest priority in developing Recovery Colleges. The creation of the first theory-based empirically evaluated developmental checklist and fidelity measure (both downloadable at researchintorecovery.com/recollect ) for Recovery Colleges will help service users understand what Recovery Colleges offer, will inform decision making by clinicians and commissioners about Recovery Colleges, and will enable formal evaluation of their impact on students.

Mechanisms of Action and Outcomes for Students in Recovery Colleges.

OBJECTIVE: Recovery colleges are widespread, with little empirical research on how they work and the outcomes they produce. This study aimed to coproduce a change model characterizing mechanisms of action (how they work) and outcomes (their impact) for mental health service users who attend recovery colleges. METHODS: A systematized review identified all publications about recovery colleges. Inductive collaborative data analysis of 10 key publications by academic researchers and coresearchers with lived experience informed a theoretical framework for mechanisms of action and student outcomes, which was refined through deductive analysis of 34 further publications. A change model was coproduced and refined through stakeholder interviews (N=33). RESULTS: Four mechanisms of action for recovery colleges were identified: empowering environment (safety, respect, and supporting choices), enabling different relationships (power, peers, and working together), facilitating personal growth (for example, coproduced learning, strengths, and celebrating success), and shifting the balance of power through coproduction and reducing power differentials. Outcomes were change in the student (for example, self-understanding and self-confidence) and changes in the student's life (for example, occupational, social, and service use). A coproduced change model mapping mechanisms of action to outcomes was created. CONCLUSIONS: Key features differentiate recovery colleges from traditional services, including an empowering environment, enabling relationships, and growth orientation. Service users who lack confidence, those with whom services struggle to engage, those who will benefit from exposure to peer role models, and those lacking social capital may benefit most. As the first testable characterization of mechanisms and outcomes, the change model allows formal evaluation of recovery colleges.

Best practice framework for Patient and Public Involvement (PPI) in collaborative data analysis of qualitative mental health research: methodology development and refinement.

BACKGROUND: Patient and Public Involvement (PPI) in mental health research is increasing, especially in early (pre-funding) stages. PPI is less consistent in later stages, including in analysing qualitative data. The aims of this study were to develop a methodology for involving PPI co-researchers in collaboratively analysing qualitative mental health research data with academic researchers, to pilot and refine this methodology, and to create a best practice framework for collaborative data analysis (CDA) of qualitative mental health research. METHODS: In the context of the RECOLLECT Study of Recovery Colleges, a critical literature review of collaborative data analysis studies was conducted, to identify approaches and recommendations for successful CDA. A CDA methodology was developed and then piloted in RECOLLECT, followed by refinement and development of a best practice framework. RESULTS: From 10 included publications, four CDA approaches were identified: (1) consultation, (2) development, (3) application and (4) development and application of coding framework. Four characteristics of successful CDA were found: CDA process is co-produced; CDA process is realistic regarding time and resources; demands of the CDA process are manageable for PPI co-researchers; and group expectations and dynamics are effectively managed. A four-meeting CDA process was piloted to co-produce a coding framework based on qualitative data collected in RECOLLECT and to create a mental health service user-defined change model relevant to Recovery Colleges. Formal and informal feedback demonstrated active involvement. The CDA process involved an extra 80 person-days of time (40 from PPI co-researchers, 40 from academic researchers). The process was refined into a best practice framework comprising Preparation, CDA and Application phases. CONCLUSIONS: This study has developed a typology of approaches to collaborative analysis of qualitative data in mental health research, identified from available evidence the characteristics of successful involvement, and developed, piloted and refined the first best practice framework for collaborative analysis of qualitative data. This framework has the potential to support meaningful PPI in data analysis in the context of qualitative mental health research studies, a previously neglected yet central part of the research cycle.