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运用隐性测量方法的应用种族/族裔医疗保健差异研究

Applied Racial/Ethnic Healthcare Disparities Research Using Implicit Measures.

作者信息

Hagiwara Nao, Dovidio John F, Stone Jeff, Penner Louis A

机构信息

Virginia Commonwealth University.

Yale University.

出版信息

Soc Cogn. 2020 Nov;38(Suppl):s68-s97. doi: 10.1521/soco.2020.38.supp.s68. Epub 2020 Dec 1.

Abstract

Many healthcare disparities studies use the Implicit Association Test (IAT) to assess bias. Despite ongoing controversy around the IAT, its use has enabled researchers to reliably document an association between provider implicit prejudice and provider-to-patient communication (provider communication behaviors and patient reactions to them). Success in documenting such associations is likely due to the outcomes studied, study settings, and data structure unique to racial/ethnic healthcare disparities research. In contrast, there has been little evidence supporting the role of providers' implicit bias in treatment recommendations. Researchers are encouraged to use multiple implicit measures to further investigate how, why, and under what circumstances providers' implicit bias predicts provider-to-patient communication and treatment recommendations. Such efforts will contribute to the advancement of both basic social psychology/social cognition research and applied health disparities research: a better understanding of implicit social cognition and a more comprehensive identification of the sources of widespread racial/ethnic healthcare disparities, respectively.

摘要

许多医疗保健差异研究使用内隐联想测验(IAT)来评估偏见。尽管围绕IAT存在持续的争议,但其使用使研究人员能够可靠地记录医疗服务提供者的内隐偏见与医患沟通(医疗服务提供者的沟通行为以及患者对这些行为的反应)之间的关联。能够记录此类关联很可能归因于所研究的结果、研究环境以及种族/族裔医疗保健差异研究特有的数据结构。相比之下,几乎没有证据支持医疗服务提供者的内隐偏见在治疗建议中所起的作用。鼓励研究人员使用多种内隐测量方法,以进一步探究医疗服务提供者的内隐偏见如何、为何以及在何种情况下能够预测医患沟通和治疗建议。此类努力将有助于推进基础社会心理学/社会认知研究以及应用健康差异研究的发展:分别更好地理解内隐社会认知以及更全面地识别普遍存在的种族/族裔医疗保健差异的根源。

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