By: Elke Zschaebitz, DNP, APRN, FNP-BC (she/her)
Implicit bias are unconscious attitudes or stereotypes that affect our understanding, actions, and decisions without our knowledge. There are various forms of implicit bias. Implicit bias in healthcare shows up in several interconnected ways, and tackling it requires attention and intention to both structural and interpersonal dynamics. Probably the first place to check in with bias is ourselves as a collective before addressing this phenomena with students.
I attended comprehensive bias training over several years on Zoom with various colleagues interested in the subject. We were those who had time during lunch, we were interested in the subject, knowing that implicit bias exists among student applications, hiring, promotion and among subspecialty selection when thinking about residencies. A particular workshop that sticks out in my mind is an implicit bias workshop I attended twice on separate occasions in the space of 18 months.
The outcomes in a team experiment were interesting. As an interprofessional group of healthcare faculty, we broke out into 10 Zoom room groups of 6-7 faculty and were given an assignment. The assignment: Act as a second reviewer and rate a student residency portfolio for medical school residency in 15 minutes. The student’s resume/CV was included as well as the notes from the first faculty interviewer who met the candidate in person. We were tasked to rate the potential selectee on a scale of 1-10. We did not have the live candidate in front of us. We all thought—this will prevent our affinity bias for the candidate because all we had was objective data.
The student was an accomplished student with a polished CV/resume illuminating a lot of experience and engagement, however the interviewer notes demonstrated the student’s profile as being a single parent, and had noted their clothing was “slightly wrinkly or not appearing a polished as expected”. The discussion among the faculty were polite, differing but landed on a score each time, with various faculty having opinions about the candidate’s overall application. We were a mixed group of faculty with various genders and ethnicities. We were 70% female as a faculty collective group and we were deeply invested professionally about learning about various forms of bias.
When we came back to the larger Zoom room after rating our applicant, we were told we all had the same student application–the only thing that was different among us was the photo of the candidate. The surprising thing about the scenario were the broad scores (4-9 on a scale of 10) between the groups. What was the differentiating factor?
The groups only had differing photos of the candidate in gender and outward-appearance.
We had discussed the possible bias of the first rater/interviewer in our groups. We thought we were fantastic at catching the biased notes of that rater. How did we vary so broadly in our rating based on a photo?
The lowest score among the 60 faculty on two difference bias trainings was for the Caucasian female. (Remember I had attended this training twice) Could this score reflect our bias among a majority of the faculty who were Caucasian females? We didn’t have time to unpack that. But on two separate occasions, differing mix of 60 interprofessional faculty illuminated the variable personal and collective implicit bias in a group setting.
There it was. Twice. Variable scoring on the same person with differing gender and appearance with our expectations/bias in the mix. We clearly had gender and ethnic bias.
Where Bias Surfaces in IPE
Identity-based biases around race, gender, age, language, and socioeconomic background do not exist in isolation — they layer on top of professional hierarchies. Research has shown that students from underrepresented groups may experience compounded marginalization in interprofessional settings, facing both professional status bias and identity-based bias simultaneously. The workshop experience made this layering visceral rather than theoretical.
Strategies That Move the Needle
Structured reflection and debriefing after IPE activities is one of the more effective approaches to identifying and disrupting patterns of implicit bias. Activities like the one I described allowed us to experience our own biases firsthand and examine them in a group setting — a far more powerful learning moment than any lecture could provide.
Faculty development is equally critical. Facilitators who haven’t examined their own biases around professional roles, race/culture, names, or gender tend to unconsciously reproduce those biases in how they run sessions. Training faculty in recognizing macroaggressions, practicing equitable facilitation techniques, and using inclusive language has shown meaningful results.
A Question for You
What exercises or experiences have been most helpful in revealing implicit biases? I’d welcome the conversation.
References
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Fan, Y., Shepherd, L. J., Slavich, E., Waters, D., Stone, M., Abel, R., & Johnston, E. L. (2019). Gender and cultural bias in student evaluations: Why representation matters. PloS one, 14(2), e0209749. https://doi.org/10.1371/journal.pone.0209749
Resources for Further Learning
- Foundational Frameworks: Blind Spot: Hidden Biases of Good People by Mahzarin R. Banaji and Anthony G. Greenwald traces the evolution of how we understand unconscious bias.
- Systematic Overviews: The Unconscious Bias among Health Professionals: A Scoping Review examines the broad medical and social impacts of implicit bias in clinical settings.
- The Implicit Association Test (IAT): Developed around 1995 by Anthony Greenwald and Mahzarin Banaji, the IAT demonstrated that automatic mental associations operate independently of explicit, conscious beliefs. The test is hosted by the nonprofit Project Implicit, co-founded in 1998 by researchers from Harvard University, the University of Virginia, and the University of Washington.
- Prevalence in Professional Fields: Systematic reviews such as the Implicit Bias in Healthcare Professionals Review show that unconscious bias is widespread and can correlate with variations in clinical decision-making and patient-provider interactions.
- Dissociation from Intent: Studies like Implicit Bias among Physicians and its Prediction of Thrombolysis Decisions reveal a clear statistical dissociation between clinicians’ reported egalitarian views and their measured implicit preferences.
Photo curtsey of IStock
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