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Premium The Development of a Classification Schema for Arts‐Based Approaches to Knowledge Translation
Author(s)
Archibald Mandy M.,
Caine Vera,
Scott Shan D.
Publication year2014
Publication title
worldviews on evidence‐based nursing
Resource typeJournals
PublisherWiley-Blackwell
ABSTRACT Background Arts‐based approaches to knowledge translation are emerging as powerful interprofessional strategies with potential to facilitate evidence uptake, communication, knowledge, attitude, and behavior change across healthcare provider and consumer groups. These strategies are in the early stages of development. To date, no classification system for arts‐based knowledge translation exists, which limits development and understandings of effectiveness in evidence syntheses. Purpose We developed a classification schema of arts‐based knowledge translation strategies based on two mechanisms by which these approaches function: (a) the degree of precision in key message delivery, and (b) the degree of end‐user participation. We demonstrate how this classification is necessary to explore how context, time, and location shape arts‐based knowledge translation strategies. Discussion Classifying arts‐based knowledge translation strategies according to their core attributes extends understandings of the appropriateness of these approaches for various healthcare settings and provider groups. The classification schema developed may enhance understanding of how, where, and for whom arts‐based knowledge translation approaches are effective, and enable theorizing of essential knowledge translation constructs, such as the influence of context, time, and location on utilization strategies. Linking Evidence to Action The classification schema developed may encourage systematic inquiry into the effectiveness of these approaches in diverse interprofessional contexts.
Subject(s)art , artificial intelligence , biochemistry , chemistry , computer science , gene , information retrieval , messenger rna , natural language processing , schema (genetic algorithms) , the arts , translation (biology) , visual arts
Language(s)English
SCImago Journal Rank1.052
H-Index49
eISSN1741-6787
pISSN1545-102X
DOI10.1111/wvn.12053

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