International Journal of Qualitative Methods最新文献

Qualitative research is important to advance health equity as it offers nuanced insights into structural determinants of health inequities, amplifies the voices of communities directly affected by health inequities, and informs community-based interventions. The scale and frequency of public health crises have accelerated in recent years (e.g., pandemic, environmental disasters, climate change). The field of public health research and practice would benefit from timely and time-sensitive qualitative inquiries for which a practical approach to qualitative data analysis (QDA) is needed. One useful QDA approach stemming from sociology is flexible coding. We discuss our practical experience with a team-based approach using flexible coding for qualitative data analysis in public health, illustrating how this process can be applied to multiple research questions simultaneously or asynchronously. We share lessons from this case study, while acknowledging that flexible coding has broader applicability across disciplines. Flexible coding provides an approachable step-by-step process that enables collaboration among coders of varying levels of experience to analyze large datasets. It also serves as a valuable training tool for novice coders, something urgently needed in public health. The structuring enabled through flexible coding allows for prioritizing urgent research questions, while preparing large datasets to be revisited many times, facilitating secondary analysis. We further discuss the benefit of flexible coding for increasing the reliability of results through active engagement with the data and the production of multiple analytical outputs.

To contribute to healthcare improvements, qualitative health research must adapt to the demanding pace of constantly changing healthcare practices and policies. To meet this challenge, researchers need methods for rigorous and rapid data analysis. This article introduces the Rapid Group Analysis Process (Rap-GAP), a new approach for rapid qualitative data analysis. This method is more efficient than other rapid qualitative analysis methods. It allows for the direct involvement of diverse participants in the analysis process, including patients or healthcare decision-makers with limited qualitative research experience, while keeping the analysis grounded in the primary data (e.g., transcripts). These attributes make Rap-GAP a unique and valuable alternative to traditional qualitative analysis. This article describes the 5-step Rap-GAP process and 3 case studies that demonstrate how to use the method and adapt it for different analytical goals. Future research will evaluate and describe the outcomes of Rap-GAP compared to traditional qualitative analysis.

Community Health Workers, promotores, and navigators (henceforth, CHWs) emerged as critical members of the public health workforce addressing social, economic, and health inequities worsened by the COVID-19 pandemic. While there is increasing appreciation for and utilization of CHW models, and recognition of the importance of tailoring and innovating these models during the pandemic, few studies have examined the processes of change by which CHW models operated during the COVID-19 pandemic, and factors that facilitated or constrained CHW health equity efforts. This protocol paper describes and reflects on the research methodology used in our qualitative study focused on CHWs. The CATALYST study aims to examine the roles that CHWs served during the COVID-19 pandemic and facilitators and barriers related to CHW health equity strategies. This qualitative study incorporates the lived experiences of CHWs, low-income communities of color whom CHWs engaged, and institutional representatives and policymakers familiar with locally implemented CHW models during the pandemic. Through a community-based participatory research process, this study involves an abductive qualitative approach to data collection and analysis. We integrate community member expertise alongside CHW and health equity frameworks in designing the research questions and data collection process. Additionally, we use an analytic approach that combines inductive (drawn from qualitative data) and deductive codes (drawn from theoretical frameworks and practice-based evidence integrated through a participatory research process) and nimbly leverages flexible coding to address inductive themes and practice-based questions. Our collaborative process offers concrete strategies to develop qualitative research protocols with community partners, with evidence used to inform policy, programmatic, and relational changes to support and amplify CHW models to promote community health and health equity.