Addressing power dynamics in community-engaged research partnerships - Lauri Andress; Tristen Hall; Sheila Davis; Judith Levine; Kimberly Cripps; Dominique Guinn (SpringerOpen, 2020)
Abstract Background Successful community-engaged research depends on the quality of the collaborative partnerships between community -members and academic researchers and may take several forms depending on the purpose which dictates the degree to which power dynamics are handled within the collaborative arrangement. Methods To understand the power dynamics and related concepts within community-engaged research arrangements, a secondary analysis of an existing qualitative data set was undertaken. Two models of community-engaged research, a review of literature, and the applied experiences of researchers familiar with community engagement practices confirmed the power dynamics concepts used to carry out the analysis of the qualitative data set according to the principles of directed content analysis. This analysis yielded quotes on power dynamics and related issues. Tools to address the power dynamics exposed by the quotes were selected using the literature and lived experience of the researchers. Finally, to ensure trustworthiness, the selected quotes on power dynamics and the recommended tools were subjected to naturalistic treatment using peer debriefings and triangulation. Results Analysis of existing qualitative data made clear that community-engaged research between health practitioners and communities may take several forms depending on the purpose and dictate how power dynamics, including inequities, biases, discrimination, racism, rank and privilege, are handled within the collaborative arrangement. Three tools including implicit bias training, positionality, and structural competency may be used to address power dynamics and related concepts. Conclusion Analysis of the qualitative data set highlighted the power dynamics within different community-engaged research models and the tools that may be used to address inequitable power dynamics including implicit bias training, positionality, and structural competency.