The article "Designing A Mixed Methods Study In Primary Care" by John W. Creswell, Michael D. Fetters, and Nataliya V. Ivankova evaluates five mixed methods studies in primary care and proposes three models to enhance the rigor of such investigations. The authors identify criteria from the social and behavioral sciences to analyze the studies, focusing on the rationale for mixing quantitative and qualitative data, data collection and analysis, priority given to either type of data, implementation sequence, and integration of data. The five studies analyzed show variations in these criteria, with three including a rationale for mixing data and diverse forms of priority. The authors recommend three models: the Instrument Design Model, which prioritizes quantitative data and integrates qualitative data during data analysis; the Triangulation Design Model, which gives equal priority to both types of data and integrates them in the results phase; and the Data Transformation Design Model, which is suitable for correlational designs and integrates data at the data analysis stage. The article discusses the limitations of the study and suggests future research directions, emphasizing the importance of expertise and resources in conducting mixed methods research in primary care.The article "Designing A Mixed Methods Study In Primary Care" by John W. Creswell, Michael D. Fetters, and Nataliya V. Ivankova evaluates five mixed methods studies in primary care and proposes three models to enhance the rigor of such investigations. The authors identify criteria from the social and behavioral sciences to analyze the studies, focusing on the rationale for mixing quantitative and qualitative data, data collection and analysis, priority given to either type of data, implementation sequence, and integration of data. The five studies analyzed show variations in these criteria, with three including a rationale for mixing data and diverse forms of priority. The authors recommend three models: the Instrument Design Model, which prioritizes quantitative data and integrates qualitative data during data analysis; the Triangulation Design Model, which gives equal priority to both types of data and integrates them in the results phase; and the Data Transformation Design Model, which is suitable for correlational designs and integrates data at the data analysis stage. The article discusses the limitations of the study and suggests future research directions, emphasizing the importance of expertise and resources in conducting mixed methods research in primary care.