Monika Kastner1, Jesmin Antony2, Charlene Soobiah3, Sharon E Straus4, Andrea C Tricco1. 1. Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, Toronto, Ontario M5B 1W8, Canada; Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Health Sciences Building, 155 College Street, 6th floor, Toronto, Ontario M5T 3M7, Canada. 2. Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, Toronto, Ontario M5B 1W8, Canada. 3. Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, Toronto, Ontario M5B 1W8, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Health Sciences Building, 155 College Street, Suite 425, Toronto, Ontario M5T 3M6, Canada. 4. Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, Toronto, Ontario M5B 1W8, Canada; Department of Geriatric Medicine, Faculty of Medicine, University of Toronto, 27 King's College Circle, Toronto, Ontario M5S 1A1, Canada. Electronic address: Sharon.Straus@utoronto.ca.
Abstract
OBJECTIVE: To compare and contrast different knowledge synthesis methods and map their specific steps through a scoping review to gain a better understanding of how to select the most appropriate knowledge synthesis method to answer research questions of complex evidence. STUDY DESIGN AND SETTING: Electronic databases were searched to identify studies reporting emerging knowledge synthesis methods (e.g., Realist review) across multidisciplinary fields. Two reviewers independently selected studies and abstracted data for each article. RESULTS: We synthesized diverse, often conflicting evidence to identify 12 unique knowledge synthesis methods and 13 analysis methods. We organized the 12 full knowledge synthesis methods according to their purpose, outputs and applicability for practice and policy, as well as general guidance on formulating the research question. To make sense of the overlap across these knowledge synthesis methods, we derived a conceptual algorithm to elucidate the process for selecting the optimal knowledge synthesis methods for particular research questions. CONCLUSION: These findings represent a preliminary understanding on which we will base further advancement of knowledge in this field. As part of next steps, we will convene a meeting of international leaders in the field aimed at clarifying emerging knowledge synthesis approaches.
OBJECTIVE: To compare and contrast different knowledge synthesis methods and map their specific steps through a scoping review to gain a better understanding of how to select the most appropriate knowledge synthesis method to answer research questions of complex evidence. STUDY DESIGN AND SETTING: Electronic databases were searched to identify studies reporting emerging knowledge synthesis methods (e.g., Realist review) across multidisciplinary fields. Two reviewers independently selected studies and abstracted data for each article. RESULTS: We synthesized diverse, often conflicting evidence to identify 12 unique knowledge synthesis methods and 13 analysis methods. We organized the 12 full knowledge synthesis methods according to their purpose, outputs and applicability for practice and policy, as well as general guidance on formulating the research question. To make sense of the overlap across these knowledge synthesis methods, we derived a conceptual algorithm to elucidate the process for selecting the optimal knowledge synthesis methods for particular research questions. CONCLUSION: These findings represent a preliminary understanding on which we will base further advancement of knowledge in this field. As part of next steps, we will convene a meeting of international leaders in the field aimed at clarifying emerging knowledge synthesis approaches.
Authors: Andrea C Tricco; Wasifa Zarin; Patricia Rios; Ba' Pham; Sharon E Straus; Etienne V Langlois Journal: BMJ Open Date: 2016-12-23 Impact factor: 2.692
Authors: Andrea C Tricco; Patricia Rios; Wasifa Zarin; Roberta Cardoso; Sanober Diaz; Vera Nincic; Alekhya Mascarenhas; Sabrina Jassemi; Sharon E Straus Journal: PLoS One Date: 2018-07-26 Impact factor: 3.240