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Record W4303859531 · doi:10.1186/s13033-022-00559-2

The impacts of implementing recovery innovations: a conceptual framework grounded in qualitative research

2022· article· en· W4303859531 on OpenAlexafffundabout
Myra Piat, Megan Wainwright, Marie-Pier Rivest, Eleni Sofouli, Tristan von Kirchenheim, Hélène Albert, Regina Casey, Lise Labonté, Joseph J. O’Rourke, Sébastien LeBlanc

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de MonctonUniversity of British ColumbiaMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCFonds de Recherche du Québec - SantéFondation de la recherche en santé du Nouveau-BrunswickResearch Manitoba
KeywordsStakeholderMental healthService providerGrounded theoryQualitative researchAxial codingKnowledge managementConceptual frameworkDyadService (business)Process managementAction researchStakeholder engagementBusinessPublic relationsPsychologyMarketingSociologyComputer sciencePolitical scienceTheoretical sampling

Abstract

fetched live from OpenAlex

BACKGROUND: Implementing mental health recovery into services is a policy priority in Canada and globally. To that end, a 5 year study was undertaken with seven organisations providing mental health and housing services to people living with a mental health challenge to implement guidelines for the transformation of services and systems towards a recovery-orientation. Multi-stakeholder implementation teams were established and a facilitated process guided teams to choosing and planning for the implementation of one recovery innovation. The recovery innovations chosen were hiring peer support workers, Wellness Recovery Action Planning (WRAP), a family support group, and staff recovery training. METHODS: This study reports on data collected at the post-implementation stage. 90 service users, service providers, family members, managers, other actors and knowledge users participated in 41 group, individual or dyad semi-structured interviews. The interview guides included open-ended questions eliciting participants' impressions regarding the impact of implementing the innovation on service users, service providers and organisations. We applied a collaborative qualitative content analysis approach in NVivo12 to coding and interpreting the data generated from these questions. RESULTS: Eighteen impacts of implementing recovery innovations from the perspectives of diverse stakeholder groups were identified. Three impacts of working as an implementation team member and as part of a research project were also identified. Impacts were developed into a conceptual framework organised around four overall categories of impact: Ways of being, Ways of interacting, Ways of thinking, and Ways of operating and doing business. CONCLUSIONS: The IMpacts of Recovery Innovations (IMRI) framework version 1 can assist researchers, evaluators and decision-makers identify, explore and understand impact in the context of recovery innovations. The framework helps fill a gap in conceptualising service and organisation-level impacts. Future research is needed to validate the framework and map it to existing methods for studying impact.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.457
GPT teacher head0.619
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes3
Has abstractyes

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