Implicit Association Test Alone Is Not Sufficient to Increase Underrepresented Minority Representation in Physician Assistant Programs.

PURPOSE: Physician assistant (PA) program matriculants are consistently less diverse than the US population. This study evaluates whether administration of an Implicit Association Test (IAT) to PA program admission committees is associated with changes in the likelihood of (1) receiving an admission interview, (2) receiving an offer of admission, and (3) matriculation of individuals underrepresented in medicine (URiM). METHODS: Admission committees from 4 PA programs participated in an IAT before the 2019/2020 admissions cycle. Applicant outcome data (n = 5796) were compared with 2018/2019 cycle (n = 6346). Likelihood of URiM students receiving offers to interview, offers of admission, and matriculation were evaluated using random effects multiple logistic regression models. Fully adjusted random effects models included URiM status, year (control vs. intervention), multiplicative interaction terms between URiM and year, applicant age, and undergraduate grade point average (GPA) Secondary analyses examined associations of each race/ethnicity individually. RESULTS: Underrepresented in medicine status, age, and GPA were significantly associated with all admission outcomes ( P < .05). The intervention effect was not statistically significant. In sensitivity analyses examining each individual race rather than URiM status, our results did not importantly differ. CONCLUSION: Findings suggest admission committee member participation in IAT before admissions had no significant impact on the likelihood of admission of URiM students. This may suggest that making individuals aware of their implicit biases is not, in and of itself, sufficient to meaningfully affect the diversity of PA program admission metrics.

Feasibility of an automated interview grounded in multiple mini interview (MMI) methodology for selection into the health professions: an international multimethod evaluation.

OBJECTIVES: Global, COVID-driven restrictions around face-to-face interviews for healthcare student selection have forced admission staff to rapidly adopt adapted online systems before supporting evidence is available. We have developed, what we believe is, the first automated interview grounded in multiple mini-interview (MMI) methodology. This study aimed to explore test-retest reliability, acceptability and usability of the system. DESIGN, SETTING AND PARTICIPANTS: Multimethod feasibility study in Physician Associate programmes from two UK and one US university during 2019-2020. PRIMARY, SECONDARY OUTCOMES: Feasibility measures (test-retest reliability, acceptability and usability) were assessed using intraclass correlation (ICC), descriptive statistics, thematic and content analysis. METHODS: Volunteers took (T1), then repeated (T2), the automated MMI, with a 7-day interval (±2) then completed an evaluation questionnaire. Admission staff participated in focus group discussions. RESULTS: Sixty-two students and seven admission staff participated; 34 students and 4 staff from UK and 28 students and 3 staff from US universities. Good-excellent test-retest reliability was observed at two sites (US and UK2) with T1 and T2 ICC between 0.65 and 0.81 (p<0.001) when assessed by individual total scores (range 80.6-119), station total scores 0.6-0.91, p<0.005 and individual site (≥0.79 p<0.001). Mean test re-test ICC across all three sites was 0.82 p<0.001 (95% CI 0.7 to 0.9). Admission staff reported potential to reduce resource costs and bias through a more objective screening tool for preselection or to replace some MMI stations in a 'hybrid model'. Maintaining human interaction through 'touch points' was considered essential. Users positively evaluated the system, stating it was intuitive with an accessible interface. Concepts chosen for dynamic probing needed to be appropriately tailored. CONCLUSION: These preliminary findings suggest that the system is reliable, generating consistent scores for candidates and is acceptable to end users provided human touchpoints are maintained. Thus, there is evidence for the potential of such an automated system to augment healthcare student selection.

Increasing Racial and Ethnic Diversity at the University of Utah Physician Assistant Program.

BACKGROUND AND OBJECTIVES: Among the oldest in the nation, the University of Utah Physician Assistant Program (UPAP) serves the state of Utah and surrounding areas and is a division of the Department of Family and Preventive Medicine. Recognizing the need to produce health care providers from diverse racial and ethnic backgrounds, UPAP instituted structural changes to improve student compositional diversity. This paper is a presentation and evaluation of the changes made to determine their relationship with compositional diversity, ultimate practice setting, and national rankings. METHODS: UPAP changed diversity messaging, curriculum, efforts in admissions, recruitment, and retention to improve the representation of Black, Latinx, American Indian/Alaska Native, and Native Hawaiian/Other Pacific Islander students, as well as those from educationally and economically disadvantaged backgrounds. RESULTS: UPAP tripled the number of underrepresented minority matriculated students over the course of five admitted classes, while simultaneously increasing the proportion of students from educationally or economically disadvantaged backgrounds. UPAP maintains both high boards pass rate and top national rankings, (number two ranking in public physician assistant program and number four overall program in the United States). CONCLUSIONS: The UPAP experience demonstrates that intentional diversity efforts are associated with improvement in racial/ethnic diversity and national rankings. Other medical school graduate programs, specifically the medical doctor (MD), public health, and basic science programs can use this model to improve their compositional diversity.