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A mixed-method simulated patient approach to explore implicit bias in health care: A feasibility study in community
Jack C Collins1, Megan MacKenzie2, Carl R Schneider1
1Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Background:
Implicit bias is prejudice shown towards an individual or group without consciously meaning to do so. This bias may occur due to age, race, and gender among other factors. Implicit bias has been examined and identified in a variety of health care professionals, primarily using Implicit Association Tests. To date, literature has not examined implicit bias using covert simulated patients in a naturalistic setting, or in practicing community pharmacists.
Objective:
To develop and pilot a novel method for exploring and reporting on implicit racial bias by community pharmacists in their practice setting.
Methods:
Four female actors of different racial and ethnic backgrounds (Caucasian Australian, East Asian, Middle Eastern, South Asian) completed simulated patient visits at eight community pharmacies in Sydney, Australia between February-March 2019. Actors provided scripted requests for assistance with symptoms or products relating to women's health ailments (cystitis, dysmenorrhea, emergency contraception, vaginal thrush) at a rate of one visit per pharmacy per week. Visits were audio-recorded. Semi-structured interviews were conducted with actors immediately post-visit. Transcriptions of visits and interviews were analyzed through discourse analysis.
Results:
Thirty-two vists were completed. Discourse analysis identified 3 discursive frames of 'caring', 'neutral, and 'abrupt' and enabled identification of potential differences between pharmacists when responding to actors of different racial and ethnic backgrounds. Consultation length, number of products sold, and number of questions asked did not significantly differ between visits.
Conclusions:
This novel method proved feasible and future work could adapt this method to different practitioners and simulated patient demographics to explore different types of implicit bias in a number of naturalistic settings.
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