A Framework for Integrating Implicit Bias Recognition Into Health Professions Education

Javeed Sukhera*, Chris Watling

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Existing literature on implicit bias is fragmented and comes from a variety of fields like cognitive psychology, business ethics, and higher education, but implicit-bias-informed educational approaches have been underexplored in health professions education and are difficult to evaluate using existing tools. Despite increasing attention to implicit bias recognition and management in health professions education, many programs struggle to meaningfully integrate these topics into curricula. The authors propose a six-point actionable framework for integrating implicit bias recognition and management into health professions education that draws on the work of previous researchers and includes practical tools to guide curriculum developers. The six key features of this framework are creating a safe and nonthreatening learning context, increasing knowledge about the science of implicit bias, emphasizing how implicit bias influences behaviors and patient outcomes, increasing self-awareness of existing implicit biases, improving conscious efforts to overcome implicit bias, and enhancing awareness of how implicit bias influences others. Important considerations for designing implicit-bias-informed curricula-such as individual and contextual variables, as well as formal and informal cultural influences-are discussed. The authors also outline assessment and evaluation approaches that consider outcomes at individual, organizational, community, and societal levels. The proposed framework may facilitate future research and exploration regarding the use of implicit bias in health professions education.
Original languageEnglish
Pages (from-to)35-40
Number of pages6
JournalAcademic Medicine
Volume93
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • MEDICAL-STUDENTS
  • ASSOCIATION TEST
  • PUBLIC-HEALTH
  • AUTOMATIC PREJUDICE
  • SELF-REGULATION
  • CARE PROVIDERS
  • GENDER BIAS
  • RACIAL BIAS
  • COMPETENCE
  • DISPARITIES

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