Adelaide Nursing School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia.
Green Templeton College, University of Oxford, Oxford, UK.
Int J Health Policy Manag. 2018 Mar 1;7(3):231-243. doi: 10.15171/ijhpm.2017.79.
Many representations of the movement of healthcare knowledge through society exist, and multiple models for the translation of evidence into policy and practice have been articulated. Most are linear or cyclical and very few come close to reflecting the dense and intricate relationships, systems and politics of organizations and the processes required to enact sustainable improvements. We illustrate how using complexity and network concepts can better inform knowledge translation (KT) and argue that changing the way we think and talk about KT could enhance the creation and movement of knowledge throughout those systems needing to develop and utilise it. From our theoretical refinement, we propose that KT is a complex network composed of five interdependent sub-networks, or clusters, of key processes (problem identification [PI], knowledge creation [KC], knowledge synthesis [KS], implementation [I], and evaluation [E]) that interact dynamically in different ways at different times across one or more sectors (community; health; government; education; research for example). We call this the KT Complexity Network, defined as a network that optimises the effective, appropriate and timely creation and movement of knowledge to those who need it in order to improve what they do. Activation within and throughout any one of these processes and systems depends upon the agents promoting the change, successfully working across and between multiple systems and clusters. The case is presented for moving to a way of thinking about KT using complexity and network concepts. This extends the thinking that is developing around integrated KT approaches. There are a number of policy and practice implications that need to be considered in light of this shift in thinking.
存在许多关于医疗保健知识在社会中传播的表述，也有许多将证据转化为政策和实践的模式。大多数都是线性或周期性的，很少有能够反映出组织的复杂关系、系统和政治以及实施可持续改进所需的过程。我们说明了如何使用复杂性和网络概念来更好地为知识转化（KT）提供信息，并认为改变我们思考和谈论 KT 的方式可以增强需要开发和利用知识的那些系统中知识的创造和流动。从我们的理论细化中，我们提出 KT 是由五个相互依存的关键过程子网络（或集群）组成的复杂网络，这些过程包括问题识别[PI]、知识创造[KC]、知识综合[KS]、实施[I]和评估[E]，它们在不同的时间和不同的部门（例如社区、卫生、政府、教育、研究等）以不同的方式动态交互。我们称之为 KT 复杂性网络，定义为一个网络，它优化了有效、适当和及时地向需要知识的人创造和转移知识，以改善他们的工作。这些过程和系统中的任何一个内部和整个过程的激活都取决于推动变革的代理人，他们需要成功地跨越和连接多个系统和集群。需要提出一个使用复杂性和网络概念来思考 KT 的案例。这扩展了围绕综合 KT 方法发展的思维。鉴于这种思维方式的转变，需要考虑许多政策和实践方面的影响。
