Abstract
Background and objectives: Health disparities are a growing concern in health care. Research provides ample evidence of bias in patient care and mistrust between patient and providers in ways that could perpetuate health care disparities. This study aimed to review existing literature on implicit bias (or unconscious bias) in healthcare settings and determine studies that have considered adverse effects of bias of more than one domain of social identity (e.g., race and gender bias) in health care.
 Methods: This is a systematic review of articles using databases such as EBSCO, Embase, CINAHL, COCHRANE, Google Scholar, PsychINFO, Pub Med, and Web of Science. Search terms included implicit bias, unconscious bias, healthcare, and public health. The inclusion criteria included studies that assessed implicit bias in a healthcare setting, written in English, and published from 1997-2018.
 Results: Thirty-five articles met the selection criteria – 15 of which examined race implicit bias, ten examined weight bias, four assessed race and social class, two examined sexual orientation, two focused on mental illness, one measured race and sexual orientation, and another investigated age bias.
 Conclusions: Studies that measured more than one domain of social identity of an individual did so separately without investigating how the domains overlapped. Implicit Association Test (IAT) is a widely used psychological test which is used to determine existence of an implicit bias in an individual. However, this study did not find any use of an instrument that could assess implicit bias toward multiple domains of social identities. Because of possible multiplicative effects of several biases affecting a single entity, this study suggests the importance of developing a tool in measuring intersectionality of biases.
 IMC J Med Sci 2019; 13(1): 005
Highlights
Gender inequality is a major social issue which may adversely affect women’s health in developing countries
Studies were eligible for inclusion if they met the following criteria: 1) published in years 19972018; 2) assessed implicit bias in a healthcare setting; 3) the study population being patients or providers, and 4) the articles written in English
A number of statistical methods have been proposed for testing intersectionality. These are: 1) The Hierarchical Classes Analysis (HICLAS) in which subgroup differences are examined [16]; 2) Crosstabulation which was used in a study by Covarrubias (2011) [17]; and 3) Logistic Regression has been used in a number of studies, especially with an addition of the multiplicative interaction term [18,19], as well as by creating pattern of association in multiple domains of implicit bias using Latent Class Analysis [20]
Summary
Gender inequality is a major social issue which may adversely affect women’s health in developing countries. Intersectionality is a theoretical framework for understanding how several social identities such as IMC J Med Sci 2019; 13(1): 005 race, gender, socioeconomic status, sexual orientation, disability etc., intersect on a micro level of individual experience to show interlocking systems of privilege and oppression (i.e., racism, sexism, heterosexism, classism, etc.) at the macro socialstructural level [1,2]. This study aimed to review existing literature on implicit bias (or unconscious bias) in healthcare settings and determine studies that have considered adverse effects of bias of more than one domain of social identity (e.g., race and gender bias) in health care
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