Related Experiment Video
Updated: Oct 24, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
Published on: January 15, 2017
Recommendations and Guidelines for the Use of Simulation to Address Structural Racism and Implicit Bias
Samreen Vora1, Brittany Dahlen, Mark Adler
1From the Simulation Program (S.V.), Children's Minnesota, Minneapolis, MN; Center for Professional Development and Practice (B.D.), Children's Minnesota, Minneapolis, MN; Department of Pediatrics and Medical Education (M.A.), Feinberg School of Medicine, Northwestern University, Chicago, IL; Department of Emergency Medicine (D.K.), Columbia University Vagelos College of Physicians and Surgeons, New York City, NY; Department of Pediatrics (V.F.J.), University of Louisville, Louisville, KY; Division of Education and Training (S.K.), The University of Texas MD Anderson Cancer Center, Houston, TX; and Department of Pediatricsa (A.C.), University of Louisville, Norton Children's Hospital, Louisville, KY.
Summary Statement:
Simulation-based education is a particularly germane strategy for addressing the difficult topic of racism and implicit bias due to its immersive nature and the paradigm of structured debriefing. Researchers have proposed actionable frameworks for implicit bias education, particularly outlining the need to shift from recognition to transformation, with the goal of changing discriminatory behaviors and policies. As simulation educators tasked with training health care professionals, we have an opportunity to meet this need for transformation. Simulation can shift behaviors, but missteps in design and implementation when used to address implicit bias can also lead to negative outcomes. The focus of this article is to provide recommendations to consider when designing simulation-based education to specifically address racism and implicit bias.
More Related Videos
Related Concept Videos
Halo Effect
Stereotypes, Prejudice, and Discrimination
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Stereotype Content Model
Surveys
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

