Klingberg Sonja, Stalmeijer Renée E, Varpio Lara
SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Department of Educational Development and Research, Faculty of Health, Medicine and Life Sciences, School of Health Professions Education, Maastricht University, Maastricht, The Netherlands.
Med Teach. 2024 May;46(5):603-610. doi: 10.1080/0142159X.2023.2259073. Epub 2023 Sep 21.
Framework analysis methods (FAMs) are structured approaches to qualitative data analysis that originally stem from large-scale policy research. A defining feature of FAMs is the development and application of a matrix-based analytical framework. These methods can be used across research paradigms and are thus particularly useful tools in the health professions education (HPE) researcher's toolbox. Despite their utility, FAMs are not frequently used in HPE research. In this AMEE Guide, we provide an overview of FAMs and their applications, situating them within specific qualitative research approaches. We also report the specific characteristics, advantages, and disadvantages of FAMs in relation to other popular qualitative analysis methods. Using a specific type of FAM-i.e. the framework method-we illustrate the stages typically involved in doing data analysis with an FAM. Drawing on Sandelowski and Barroso's continuum of data transformation, we argue that FAMs tend to remain close to raw data and be descriptive or exploratory in nature. However, we also illustrate how FAMs can be harnessed for more interpretive analyses. We propose that FAMs are valuable resources for HPE researchers and demonstrate their utility with specific examples from the HPE literature.
框架分析方法(FAMs)是定性数据分析的结构化方法,最初源于大规模政策研究。FAMs的一个显著特征是基于矩阵的分析框架的开发和应用。这些方法可用于各种研究范式,因此是健康职业教育(HPE)研究人员工具包中特别有用的工具。尽管FAMs具有实用性,但在HPE研究中并不经常使用。在本AMEE指南中,我们概述了FAMs及其应用,将它们置于特定的定性研究方法之中。我们还报告了FAMs相对于其他流行定性分析方法的具体特征、优点和缺点。使用一种特定类型的FAM——即框架方法——我们说明了使用FAM进行数据分析通常涉及的阶段。借鉴桑德洛维茨和巴罗斯的数据转换连续体,我们认为FAMs倾向于贴近原始数据,本质上具有描述性或探索性。然而,我们也说明了如何利用FAMs进行更多的解释性分析。我们认为FAMs是HPE研究人员的宝贵资源,并通过HPE文献中的具体例子展示了它们的实用性。