Pro-Con Debate: Consideration of Race, Ethnicity, and Gender Is Detrimental to Successful Mentorship.

Anesthesiology remains a specialty with low representation of women or members of racial and ethnic groups considered underrepresented in medicine (UiM). In the United States, women account for 33% of anesthesiology residents, while physicians identifying as Black, African American, Latinx, American Indian, or Alaska Native account for approximately 10%. Underrepresentation of these groups is even more pronounced in academic anesthesiology, especially at the senior ranks and roles, such as department chairs. Leaders in the field have recently shared recommendations for how individual departments, medical schools, hospitals, and professional organizations can create and support a more diverse anesthesiology workforce. These commentaries have often stressed the importance of mentorship for supporting women and physicians from UiM groups, including mentorship of trainees and practicing anesthesiologists seeking to advance their careers. While the value of mentorship is undisputed, it remains a matter of controversy whether race, ethnicity, or gender should be explicitly considered by mentoring programs and individual mentors. In this article, we discuss whether and how race, ethnicity, and gender should be considered in the setting of mentorship programs and the formation of individual mentoring relationships, as well as some of the potential consequences that lie therein.

Multivariate Modeling of Student Performance on NBME Subject Exams.

Aim This study sought to determine whether it was possible to develop statistical models which could be used to accurately correlate student performance on clinical subject exams based on their National Board of Medical Examiner (NBME) self-assessment performance and other variables, described below, as such tools are not currently available.  Methods Students at a large public medical school were provided fee vouchers for NBME self-assessments before clinical subject exams. Multivariate regression models were then developed based on how self-assessment performance correlated to student success on the subsequent subject exam (Medicine, Surgery, Family Medicine, Obstetrics-Gynecology, Pediatrics, and Psychiatry) while controlling for the proximity of the self-assessment to the exam, USMLE Step 1 score, and the academic quarter. Results The variables analyzed satisfied the requirements of linear regression. The correlation strength of individual variables and overall models varied by discipline and outcome (equated percent correct or percentile, Model R2 Range: 0.1799-0.4915). All models showed statistical significance on the Omnibus F-test (p<0.001). Conclusion The correlation coefficients demonstrate that these models have weak to moderate predictive value, dependent on the clinical subject, in predicting student performance; however, this varies widely based on the subject exam in question. The next step is to utilize these models to identify struggling students to determine if their use reduces failure rates and to further improve model accuracy by controlling for additional variables.

Evaluating situational judgment test use and diversity in admissions at a southern US medical school.

INTRODUCTION: Situational judgment tests have been adopted by medical schools to assess decision-making and ethical characteristics of applicants. These tests are hypothesized to positively affect diversity in admissions by serving as a noncognitive metric of evaluation. The purpose of this study was to evaluate the performance of the Computer-based Assessment for Sampling Personal Characteristics (CASPer) scores in relation to admissions interview evaluations. METHODS: This was a cohort study of applicants interviewing at a public school of medicine in the southeastern United States in 2018 and 2019. Applicants took the CASPer test prior to their interview day. In-person interviews consisted of a traditional interview and multiple-mini-interview (MMI) stations. Between subjects, analyses were used to compare scores from traditional interviews, MMIs, and CASPer across race, ethnicity, and gender. RESULTS: 1,237 applicants were interviewed (2018: n = 608; 2019: n = 629). Fifty-seven percent identified as female. Self-identified race/ethnicity included 758 White, 118 Black or African-American, 296 Asian, 20 Native American or Alaskan Native, 1 Native Hawaiian or Other Pacific Islander, and 44 No response; 87 applicants identified as Hispanic. Black or African-American, Native American or Alaskan Native, and Hispanic applicants had significantly lower CASPer scores than other applicants. Statistically significant differences in CASPer percentiles were identified for gender and race; however, between subjects, comparisons were not significant. CONCLUSIONS: The CASPer test showed disparate scores across racial and ethnic groups in this cohort study and may not contribute to minimizing bias in medical school admissions.