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Using Simulation-Based Learning with Standardized Patients (SP) in an Implicit Bias Mitigation Clinician Training

Jennifer Tjia1, Michele Pugnaire2, Joanne Calista2,3

  • 1Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, USA.

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|June 16, 2023
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Summary
This summary is machine-generated.

This study refined an implicit bias training program for healthcare professionals using community input and simulation. The revised program separates didactics from simulations, enhancing trainee and patient advocate experiences.

Keywords:
bias mitigationclinician trainingcommunity engagementcurriculumimplicit biasstandardized patients

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Area of Science:

  • Medical Education
  • Health Disparities Research
  • Implicit Bias Training

Background:

  • Healthcare disparities persist, influenced by implicit bias.
  • Clinical trainees require effective bias recognition and management skills.
  • Community engagement is crucial for developing relevant healthcare interventions.

Purpose of the Study:

  • To describe the development and refinement of an implicit bias recognition and management training program.
  • To integrate community feedback into bias mitigation strategies for clinical trainees.
  • To enhance the training program's effectiveness through iterative design.

Main Methods:

  • Participatory action research involving community members and academic faculty.
  • Development of a two-session training program for medical residents and nursing students.
  • Utilized didactics, Implicit Association Test (IAT), and simulation-based encounters with standardized patients (SPs).

Main Results:

  • Initial program refinement addressed trainee and SP feedback regarding session sequencing and faculty support.
  • Revised program separated didactic content from IAT and SP simulations.
  • Enhanced program incorporated more interactive discussions on identity, race, ethnicity, and structural racism.

Conclusions:

  • An implicit bias training program using simulation and community engagement is feasible.
  • Tailoring content to local patient populations enhances relevance.
  • Further research is needed to evaluate the broader impact and replicability of this training model.