The CFIR Card Game: a new approach for working with implementation teams to identify challenges and strategies
Bibliographic record
Abstract
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.
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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 | 0.057 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".