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Record W3118868803 · doi:10.1186/s43058-020-00099-1

The CFIR Card Game: a new approach for working with implementation teams to identify challenges and strategies

2021· article· en· W3118868803 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueImplementation Science Communications · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British ColumbiaUniversité du Québec à Trois-RivièresDouglas Mental Health University InstituteMcGill UniversityDouglas CollegeUniversité de MonctonMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCFonds de Recherche du Québec - SantéFondation de la recherche en santé du Nouveau-BrunswickResearch Manitoba
KeywordsImplementation researchCLARITYComputer scienceQualitative researchMedicinePsychological interventionNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The Consolidated Framework for Implementation Research (CFIR) and the ERIC compilation of implementation strategies are key resources for identifying implementation barriers and strategies. However, their respective density and complexity make their application to implementation planning outside of academia challenging. We developed the CFIR Card Game as a way of working with multi-stakeholder implementation teams that were implementing mental health recovery into their services, to identify barriers and strategies to overcome them. The aim of this descriptive evaluation is to describe how the game was prepared, played, used and received by teams and researchers and their perception of the clarity of the CFIR constructs. METHODS: We used the new CFIR-ERIC Matching Tool v.1 to design the game. We produced a deck of cards with each of the CFIR-ERIC Matching Tool barrier narratives representing all 39 CFIR constructs. Teams played the game at the pre-implementation stage at a time when they were actively engaged in a planning process for implementing their selected recovery-oriented innovation. The teams placed each card in either the YES or NO column of the board in response to whether they anticipated experiencing this barrier in their setting. Teams were also asked about the clarity of the barrier narratives and were provided with plain language versions if unclear. Researchers completed a reflection form following the game, and participants completed an open-added questionnaire that included questions specific to the CFIR Card Game. We applied a descriptive coding approach to analysis. RESULTS: Four descriptive themes emerged from this analysis: (1) the CFIR Card Game as a useful and engaging process, (2) difficulties understanding CFIR construct barrier narratives, (3) strengths of the game's design and structure and room for improvement and (4) mediating factors: facilitator preparation and multi-stakeholder dynamics. Quantitative findings regarding the clarity of the barrier narratives were integrated with qualitative data under theme 2. Only seven of the 39 original barrier narratives were judged to be clear by all teams. CONCLUSIONS: The CFIR Card Game can be used to enhance implementation planning. Plain language versions of CFIR construct barrier narratives are needed. Our plain language versions require further testing and refining.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0060.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.745
GPT teacher head0.721
Teacher spread0.024 · 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