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    This study combined realist and participatory approaches for qualitative data analysis. Community and academic teams collaborated to analyze interviews, developing CIMO hypotheses to understand neighborhood conditions and pandemic actions.

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    Area of Science:

    • Community-based participatory research
    • Qualitative data analysis
    • Realist evaluation

    Background:

    • Realist reviews confirm participatory action research effectiveness.
    • Combining realist and participatory approaches in qualitative data analysis is novel.
    • Previous research has not integrated these methodologies.

    Purpose of the Study:

    • To investigate links between neighborhood conditions and community actions during the COVID-19 pandemic in Toronto.
    • To describe procedures for a community-university research partnership using a critical realist approach.
    • To enable full community-academic team participation in qualitative data analysis.

    Main Methods:

    • Critical realist approach applied to qualitative interviews with 46 grassroots leaders.
    • Analysis involved coding for contexts, intervention components, mechanisms, and outcomes (CIMO).
    • Sense-making workshops with community partners and interviewees to develop CIMO hypotheses.

    Main Results:

    • Developed a novel approach for mixed community-academic teams to analyze qualitative data.
    • Generated midrange CIMO hypotheses from individual interviews.
    • Facilitated collaborative sense-making to derive overall statements of impact.

    Conclusions:

    • This study presents a novel method for qualitative data analysis integrating realist and participatory approaches.
    • The described procedures enable effective collaboration between community leaders and academics.
    • This approach has significant implications for participatory research and generating impact statements.