Health interventions for self-management: the role of qualitative approaches in mixed methods research
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Abstract
The long-term success of health intervention programs for self-management is often compromised by the difficulties felt by participants to maintain adherence to prescribed behavioral changes. Mixed methods research can expand understanding and the insights of complex health research problems such as self-management in chronic conditions. The purpose of this paper is to share key contents of a symposium focusing on the application of mixed methods research in the context of person-centered education, held during the 6th World Conference on Qualitative Research. More specifically, it aims at: 1) exploring examples of implementing mixed methods research in health interventions for self-management of chronic conditions and other health risks; 2) promoting insights and knowledge on the ‘why and how of combinations’ in mixed methods studies; and 3) exploring the valuable role of qualitative strands in mixed methods in the wider context of health research. Three studies are explored as examples of application of mixed methods in self-management programs, considering the experiences of participants for changing self-care behaviors and challenges faced for maintaining such changes. Convergent and explanatory sequential designs have been used, combining questionnaires and semi-structured interviews for data collection. Data analysis included procedures such as thematic analysis and descriptive statistics (examples 1 and 3), and thematic analysis and descriptive/inferential statistics (example 2). Results show how mixed methods designs can contribute to develop self-management strategies, to be considered in planning future interventions, and to expand understanding about their impact as well. Likewise, these examples emphasize why mixed methods can bring added value both to process and results of health research. Thus, exploring the application of mixed methods into a wider context is of utmost importance since the complexity of health phenomena is growing and requires equally complex research designs to capture them.
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