文章基本信息

标题：The Implicit Association Test in health professions education: A meta-narrative review
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作者：Javeed Sukhera ; Michael Wodzinski ; Maham Rehman 等
期刊名称：Perspectives on Medical Education
印刷版ISSN：2212-2761
电子版ISSN：2212-277X
出版年度：2019
卷号：8
期号：5
页码：267-275
DOI：10.1007/s40037-019-00533-8
出版社：Springer Verlag
摘要：IntroductionImplicit bias is a growing area of interest among educators. Educational strategies used to elicit awareness of implicit biases commonly include the Implicit Association Test (IAT). Although the topic of implicit bias is gaining increased attention, emerging critique of the IAT suggests the need to subject its use to greater theoretical and empirical scrutiny. MethodsThe authors employed a meta-narrative synthesis to review existing research on the use of the IAT in health professions education. Four databases were searched using key terms yielding 1151 titles. After title, abstract and full-text screening, 38 articles were chosen for inclusion. Coding and analysis of articles sought a meaningful synthesis of educational approaches relating to the IAT, and the assumptions and theoretical positions that informed these approaches. ResultsDistinct, yet complementary, meta-narratives were found in the literature. The dominant perspective utilizes the IAT as a metric of implicit bias to evaluate the success of an educational activity. A contrasting narrative describes the IAT as a tool to promote awareness while triggering discussion and reflection. DiscussionWhether used as a tool to measure bias, raise awareness or trigger reflection, the use of the IAT provokes tension between distinct meta-narratives, posing a challenge to educators. Curriculum designers should consider the premise behind the IAT before using it, and be prepared to address potential reactions from learners such as defensiveness or criticism. Overall, findings suggest that educational approaches regarding implicit bias require critical reflexivity regarding assumptions, values and theoretical positioning related to the IAT.
关键词：Implicit bias ; Implicit Association Test ; Reflection ; Health disparities