Abstract
Knowledge translation (KT) was traditionally framed as a problem of moving research results into policy and practice. The impetus for the flow of knowledge originated with researchers constructing new knowledge and seeing its utility, or with policymakers and administrators seeing problems in practice and looking to researchers for solutions. In the 1970s, a shift in focus away from knowledge use was exemplified by Caplan’s (1979) two-communities theory, which posits that researchers and policymakers comprise two different communities with two different languages (Jacobson, Butterill, & Goering, 2003). A shift back to knowledge use with a new focus on user-centered design is evident in more recent KT models that provide frameworks for researcher and user interaction in order to build better understanding between diverse groups. The flow of knowledge from its construction in one context to its use in another context has been variously termed knowledge translation, knowledge exchange, knowledge transfer, research transfer, technology transfer, knowledge transformation, knowledge dissemination, knowledge mobilization, knowledge utilization, and research utilization. The terms are often used synonymously, but a specific term is sometimes used because it highlights a particular component of the knowledge flow process. For example, knowledge exchange implies a sharing of information between partners of equal value and focuses on the movement of knowledge between them, whereas research utilization implies the transformation of research results into usable knowledge and focuses on embedding the usable knowledge in practice. Information technologies have the potential to support knowledge translation in powerful ways. Key processes in the translation of knowledge include: (1) knowledge creation, management, and dissemination; (2) recognition of links between existing knowledge and its potential application to problems or practice; (3) translation into usable knowledge in practice; and (4) change in practice. Information technologies are a natural solution for these knowledge translation processes. For example, group and social software such as blogs and wikis support collaborative construction and sharing of knowledge; knowledge management systems support capture, storage, accessibility, and maintenance of constructed knowledge; and most Internet-based technologies support dissemination of information. Well-designed virtual communities provide online environments for the kinds of human interaction that enable collaborative exploration of ideas, that foster recognition of potential links between existing knowledge and its application to solve problems or change practice, and that inspire people to transform their practice. Data mining and artificial intelligence techniques can be used to enhance identification of potential links between knowledge in one context and problems in another context.
TopBackground
A variety of approaches to knowledge translation have been developed, most focusing on the interaction of researchers, practitioners, and policymakers to move research results into practice. KT is not inherently unidirectional (research to practice), and Lavis et al. (2001) have argued that researcher-user interaction should become standard practice in research contexts, not simply an add-on. This practice has the potential to open new communication channels from knowledge constructed in practice to new research questions and hypotheses. The five models described below demonstrate a variety of KT approaches in use, from a national initiative to a framework for individual researchers.