Using the Consolidated Framework for Implementation Research to Foster the Adoption of a New Dementia Education Game During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The pandemic of coronavirus disease 2019 challenged educators to move staff education online and explore innovative ways to motivate learning to support dementia care for patients in geriatric settings. This article presents how the Consolidated Framework for Implementation Research (CFIR) was used to support the adoption of an online dementia education game in Canadian hospitals and long-term care homes (LTC). The dementia education was codeveloped with local staff and patient partners to teach practical person-centered care communication techniques. RESEARCH DESIGN AND METHODS: CFIR guided our strategy development for overcoming barriers to implementation. Research meetings were conducted with practice leaders, frontline health care workers, and a patient partner. Our analysis examined 4 interactive domains: intervention, inner context, outer settings, and individuals involved and implementation process. RESULTS: Our analysis identified 5 effective strategies: Easy access, Give extrinsic and intrinsic rewards, Apply implementation science theory, Multiple tools, and Engagement of champion. The CFIR provided a systematic process, a comprehensive understanding of barriers, and possible enabling strategies to implement gamified dementia education. Interdisciplinary staff (n = 3,025) in 10 hospitals and 10 LTC played online games. The evaluation showed positive outcomes in knowledge improvement in person-centered dementia care. DISCUSSION AND IMPLICATIONS: Gamified education in dementia care offers a social experience and a component of fun to promote adoption. In addition, CFIR is useful for engaging stakeholders to conduct project planning and team reflection for implementation. The real-time discussion and adjustment helped overcome challenges and timely meet the needs of multiple organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".