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Can Behavior-Based Interviews Reduce Bias in Fellowship Applicant Assessment?
Melissa L Langhan1, Michael P Goldman1, Gunjan Tiyyagura1
1Departments of Pediatrics and Emergency Medicine, Section of Pediatric Emergency Medicine, Yale University School of Medicine, New Haven, Conn.
Behavior-based interviews (BBIs) successfully reduced racial disparities in fellowship applicant assessments. This structured interview approach mitigated potential biases compared to traditional methods, leading to more equitable evaluations.
Area of Science:
- Medical Education
- Psychometrics
- Diversity and Inclusion in Healthcare
Background:
- Trainee application components can introduce bias related to race or gender.
- Behavior-based interviews (BBIs) use structured questions to predict future behavior from past experiences.
- Assessing and mitigating bias in applicant selection is crucial for equitable training programs.
Purpose of the Study:
- To implement BBIs in a fellowship program.
- To compare applicant assessment by race and gender using standardized versus BBI-based tools.
- To evaluate the impact of BBIs on reducing assessment bias.
Main Methods:
- Developed BBIs and specific assessment tools in 2019-2020.
- Trained 6 of 15 faculty in BBIs.
- Applicants underwent 6 interviews (BBI or unstructured) and faculty completed standardized or BBI-specific assessments.
- Analyzed normalized scores and applicant quartiles by race and gender.
Main Results:
- Standardized assessments showed significant racial differences in scores (P=.02) and quartiles (P=.05).
- BBI scores and quartiles did not show significant racial differences (P=.18 and P=.17, respectively).
- No significant differences by gender were found for either assessment tool. Faculty and applicant surveys showed positive BBI feedback.
Conclusions:
- BBIs were successfully implemented and positively received by faculty and applicants.
- BBIs demonstrated a reduction in racial differences in applicant assessments.
- Structured tools like BBIs may improve applicant assessment by mitigating potential biases.
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