This article discusses the design of mixed methods studies in primary care, evaluating five published studies and proposing three models for such research. The authors identify five criteria for analyzing mixed methods studies: rationale for mixing data, types of data collected, priority given to quantitative or qualitative data, implementation sequence (concurrent or sequential), and integration of data. They analyze five studies published in primary care research journals, finding that three included a rationale for mixing data, with some studies emphasizing qualitative data, others quantitative, and some equal priority. Data collection involved both quantitative and qualitative methods, often conducted concurrently or sequentially. Integration of data occurred during analysis, between phases, or when reporting results. The authors recommend three models for mixed methods designs: instrument design, triangulation, and data transformation. The instrument design model prioritizes quantitative data, starting with qualitative data collection to develop an instrument. Triangulation involves collecting and analyzing both quantitative and qualitative data simultaneously, often in separate sections of the report. The data transformation model uses qualitative data to inform quantitative analysis, often through coding and counting themes. The study highlights the complexity of mixed methods research and the need for explicit models to ensure rigor. The authors note limitations in their analysis, including the limited number of studies discussed and the potential for data not being fully integrated. They conclude that mixed methods research is labor-intensive and requires expertise in both quantitative and qualitative methods. Future research should explore additional models and refine criteria for evaluating the quality of mixed methods studies in primary care.This article discusses the design of mixed methods studies in primary care, evaluating five published studies and proposing three models for such research. The authors identify five criteria for analyzing mixed methods studies: rationale for mixing data, types of data collected, priority given to quantitative or qualitative data, implementation sequence (concurrent or sequential), and integration of data. They analyze five studies published in primary care research journals, finding that three included a rationale for mixing data, with some studies emphasizing qualitative data, others quantitative, and some equal priority. Data collection involved both quantitative and qualitative methods, often conducted concurrently or sequentially. Integration of data occurred during analysis, between phases, or when reporting results. The authors recommend three models for mixed methods designs: instrument design, triangulation, and data transformation. The instrument design model prioritizes quantitative data, starting with qualitative data collection to develop an instrument. Triangulation involves collecting and analyzing both quantitative and qualitative data simultaneously, often in separate sections of the report. The data transformation model uses qualitative data to inform quantitative analysis, often through coding and counting themes. The study highlights the complexity of mixed methods research and the need for explicit models to ensure rigor. The authors note limitations in their analysis, including the limited number of studies discussed and the potential for data not being fully integrated. They conclude that mixed methods research is labor-intensive and requires expertise in both quantitative and qualitative methods. Future research should explore additional models and refine criteria for evaluating the quality of mixed methods studies in primary care.