The use of theories, frameworks, or models in knowledge translation studies in healthcare settings in China: a scoping review protocol

Abstract Background Knowledge translation (KT) theories, frameworks, and models (TFMs) can help guide and explain KT processes, and facilitate the evaluation of implementation outcomes. They play a critical role in conducting KT research and practice. Currently, little is known about the usage of TFMs in KT in Chinese healthcare settings. The aim of this scoping review is to identify which TFMs had been used for KT in healthcare settings in China, and how these TFMs were used. Methods The protocol for this scoping review is in accordance with the Arksey and O’Malley framework and further enhanced by the recommendations suggested by Levac et al. We will search 8 databases (4 Chinese and 4 English) to identify relevant studies. Four reviewers (2 for Chinese, 2 for English) will independently screen studies based on the eligibility criteria. The basic characteristic of studies and the TFMs utilization (i.e., what, why, and how) will be extracted. Methodological quality and reporting quality will be assessed using the Mixed Method Appraisal Tool (MMAT) and the Standards for Reporting Implementation Studies (StaRI) (or Standards for Quality Improvement Reporting Excellence, SQUIRE 2.0 for quality improvement studies), respectively. All the retrieved TFMs will be categorized into Nilsen’s classifications of TFMs for KT research. We will employ the qualitative content analysis approach to summarize how these TFMs have been used, and the rationale. A consultation will be conducted through a 1-h interactive virtual meeting with an expert panel of knowledge users. Discussion By conducting this scoping review, we expect to gain a comprehensive and in-depth understanding of why and how TFMs have been used in KT research and practice in China, and to identify gaps and provide recommendations for more efficient and meaningful use of TFMs in the future. Systematic review registration This review has been registered with the Open Science Framework (10.17605/OSF.IO/8NXAM)