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Circulation Conference: American Heart Association's ; 146(Supplement 1), 2022.
Article in English | EMBASE | ID: covidwho-2194383

ABSTRACT

Introduction: The goal of cardiovascular training programs is to increase recruitment of women and underrepresented in medicine (UIM) trainees. Due to the COVID-19 pandemic, virtual interviewing replaced in-person interviewing for cardiology fellowship applications in the United States. However, there is little data on the influence of these changes on fellowship candidate diversity. Method(s): In a single-center retrospective study, we identified demographic changes in the profiles of trainees who interviewed for the Stanford University Cardiovascular Fellowship Program before and after the transition to virtual interviewing (2019 and 2020 vs 2021 and 2022 cycles). UIM applicants were defined as those who self-identified Black or Hispanic. Test of proportions and t-test were used for categorical and numeric variables respectively. Result(s): Over four years, 2250 trainees applied, 212 candidates interviewed, and 24 were accepted to the fellowship. Of those interviewed, 42.0% were women, 9.9% were Black, and 9.4% were Hispanic. The proportion of UIM trainees interviewed increased from 10.5% during the in-person years to 30.2% during the virtual years (p < 0.001;Figure 1A). The increase among women was not statistically significant (40% in-person vs. 44% virtual, p = 0.58). The geographic distribution shifted over time to include lower representation from closer Western programs (40% in-person to 29% virtual) and higher representation from Northeast programs (35% in-person to 44% virtual;Figure 1B). The weighted mean distance of represented residency programs nominally increased from 1730 miles to 2067 miles away (p = 0.06). Conclusion(s): Virtual interviews were associated with a three-fold increase in UIM interviewed applicants for cardiology fellowship positions at a single center. Further research is warranted to assess the full impact of virtual interviews, as well as to understand and mitigate potential bias in the evolving selection process.

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