EMTReK Model for Advance Care Planning in Long-Term Care: Qualitative Findings from mySupport Study
Bibliographic record
Abstract
Background/Objectives: Conversations about end-of-life care or advance care planning are often difficult and emotionally challenging to initiate. Tailoring messages to the specific audiences can make these sensitive discussions more manageable and effective. The Evidence-based Model for the Transfer and Exchange of Research Knowledge (EMTReK), compromising six core components (message, stakeholders, processes, context, facilitation, and evaluation) offers a structured framework for research dissemination and knowledge transfer in palliative and long-term care settings. Knowledge translation bridges research and practice, with its effectiveness depending on stakeholder engagement, tailored communication, and systematic application of evidence in policy and practice. This study explores stakeholder perspectives on a dementia care intervention, using EMTReK as an analytical framework to examine how knowledge transfer and exchange (KTE) actions were implemented across long-term care settings. Methods: A qualitative analysis was conducted on primary data comprising case narratives from multinational research groups involved in the “Caregiver Decision Support” (mySupport) study (2019–2023). Teams from Canada, the Czech Republic, Ireland, Italy, the Netherlands, and the United Kingdom evaluated the mySupport intervention through interviews, with analysis guided by components of the EMTReK model. Results: Facilitated Family Care Conferences were found to be effective mechanisms for supporting knowledge transfer and intervention uptake in dementia care across nursing homes in Europe and Canada. Despite challenges posed by the COVID-19 pandemic, Family Care Conferences adapted through stakeholder engagement, interactive learning, and innovative communication methods. Using EMTReK as an analytical framework, the research team identified key elements that contributed to successful implementation, including the importance of flexibility to accommodate local contexts. Conclusions: The transnational application of the EMTReK model for advance care planning in long-term dementia care highlights the importance of tailored, culturally relevant knowledge translation strategies, which, despite challenges from the COVID-19 pandemic, were successfully implemented through local adaptations and diverse dissemination methods, emphasising the need for further research on their impact on resident and family outcomes.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".