Paediatrics Publications

Document Type

Article

Publication Date

1-1-2018

Journal

Academic Medicine

Volume

93

Issue

11 S

First Page

S82

Last Page

S88

URL with Digital Object Identifier

10.1097/ACM.0000000000002382

Abstract

Purpose Implicit biases worsen outcomes for underserved and marginalized populations. Once health professionals are made aware of their implicit biases, a process ensues where they must reconcile this information with their personal and professional identities. The authors sought to explore how identity influences the process of implicit bias recognition and management. Method Using constructivist grounded theory, the authors recruited 11 faculty and 10 resident participants working at an academic health science center in Canada. Interviews took place from June to October 2017. Participants took an online version of the mental illness implicit association test (IAT) which provides users with their degree of implicit dangerousness bias toward individuals with either physical or mental illness. Once they completed the IAT, participants were invited to draw a rich picture and interviewed about their picture and experience of taking their IAT. Data were analyzed using constant comparative procedures to develop focused codes and work toward the development of a deeper understanding of relationships among themes. Results Once implicit biases were brought into conscious awareness, participants acknowledged vulnerabilities which provoked tension between their personal and professional identities. Participants suggested that they reconcile these tensions through a process described as striving for the ideal while accepting the actual. Relationships were central to the process; however, residents and faculty viewed the role of relationships differently. Conclusions Striving for self-improvement while accepting individual shortcomings may provide a model for addressing implicit bias among health professionals, and relational dynamics appear to influence the process of recognizing and managing biases.

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