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EMTReK: An Evidence-based Model for the Transfer & Exchange of Research Knowledge-Five Case Studies in Palliative Care.

Cathy PayneMary J BrownSuzanne GuerinWilliam George Kernohan
Published in: SAGE open nursing (2019)
Knowledge transfer is recognized as a vital stage in evidence-informed nursing with several models available to guide the process. Although the main components commonly involve identification of messages, stakeholders, processes and contexts, the underpinning models remain largely unrefined and untested; and they need to be evaluated. We set out to explore the use of our "Evidence-based Model for Transfer & Exchange of Research Knowledge" (EMTReK) within palliative care research. Between January 2016 and May 2017, data were collected from five case studies which used the EMTReK model as a means to transfer knowledge relating to palliative care research, undertaken in Ireland. A qualitative approach was taken with thematic analysis of case documentation, semistructured interviews, and field notes from the case studies. Qualitative analysis supports the core components of EMTReK as a model of knowledge transfer and exchange in palliative care. Results focused upon identification of messages to be transferred to defined stakeholders through interactive processes that take account of context. Case study findings show how the model was interpreted and operationalized by participants and demonstrate its impact on knowledge transfer and exchange. Eight themes were drawn from the data: Credibility of the Model, Model Accessibility, Applicability to Palliative Care, A Matter of Timing, Positive Role of Facilitation, Required Resources, Enhancing Research Quality, Limitations or Areas for Further Consideration. Study participants found EMTReK to be a useful guide when making knowledge transfer plans. Success depended upon adequate facilitation and guidance. Further exploration of the model's utility is warranted.
Keyphrases
  • palliative care
  • healthcare
  • advanced cancer
  • machine learning
  • mental health
  • big data
  • artificial intelligence