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Sunday, February 26 • 11:00am - 11:45am
ARCHES - [Oral Presentation] 1. Situational Judgment Test (SJT) compared to Multiple Mini Interview (MMI), Medical College Admission Test (MCAT), and Grade Point Average (GPA)

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11:00 AM - 11:15 AM

Situational Judgment Test (SJT) compared to Multiple Mini Interview (MMI), Medical College Admission Test (MCAT), and Grade Point Average (GPA)  
B.R. Chan, J. Colbert-Getz, K. Pippitt, C. Knupp, M. Onofrietti, University of Utah School of Medicine
Abstract Body: Context Medical school admissions rely on limited data to make decisions. The limitations of the Medical College Admission Test (MCAT) and Grade Point Average (GPA) are apparent. Recent innovations, such as the Multiple Min Interview (MMI), have attempted to add additional information for admissions committees. However, MMI can be labor and time intensive, and there still can be variation in between evaluators on how they assess different scenarios. The next logical step is to better test non-cognitive skills, such as a Situational Judgement Test (SJT), where a standardized test is created. SJTs can inquire about different competencies, such as teamwork, professionalism, and communication skills. However, if an applicant does well on MCAT, GPA, or MMI, is there a positive correlation between SJT performance? We administered a MMI and SJT to 499 applicants to the University of Utah School of Medicine. Objectives To determine if SJT performance is correlated with MCAT, GPA, or MMI performance. We hypothesize that we are measuring different attributes and traits, such as teamwork skills, communication, professionalism that would not necessarily be detected with MCAT or GPA. Key Messages Comparing performance across MCAT, GPA, MMI, and SJT, we found that there was a positive, but low correlation (<0.30) between SJT across the domains. Thus, superior test taking (MCAT) and studying skills (GPA) and impromptu interview performance (MMI) was considered separate from SJT. This was found to be reassuring insofar that SJT appears to be assessing different domains. Conclusions Additional research will be required to longitudinally follow matriculated medical students in regards to these domains. 

California Longitudinal Evaluation of Admission Practices (CA-LEAP): Variability in Predictors of Acceptance to Medical Schools by Institution and Disadvantage Status  
E.J. Griffin, M. Henderson, C. Kelly, P. Franks, A. Jerant, UC-Davis, UC-San Diego
 Abstract Body: Introduction In 2014, deans of admissions from 5 University of California (UC) Medical Schools formed the California Longitudinal Evaluation of Admission Practices (CA-LEAP) consortium, supported by a grant from the Edward J. Stemmler Fund. Our study includes nearly 8,000 interviews from nearly 5000 individuals from three consecutive matriculation cycles (2011-20110) and includes information about applicant characteristics and demographics, interview method (MMI versus traditional), and admissions outcomes. The consortium has accumulated data from multiple institutions to longitudinally evaluate the relationships between applicant characteristics, interview and admissions practices, and performance outcomes in medical school and beyond. Research question We sought to assess the extent to which UC Medical Schools are using holistic review in the applicant selection process. To explore this question, we obtained qualitative information about interview practices from each school and analyzed the relationship between applicant demographics, disadvantage status, undergraduate metrics, and interview performance, and whether and acceptance offer was extended (yes/no). Methods Admissions and medical school performance data were collected and analyzed at UC Davis. Application and interview records were linked by a unique ID. Descriptive analyses were conducted to explore applicant characteristics and acceptance offer outcomes across the five schools. A series of multivariate logistic regression models were used to estimate the odds of receiving an acceptance offer versus not receiving an offer, as a function of applicant characteristics including gender, age, self-reported disadvantage status, undergraduate GPA and MCAT scores, and standardized interview performance score. Analyses were conducted within and across schools. Results Average undergraduate GPA and MCAT scores were similar among interviewees across the schools (range= range = 3.67-3.80). Schools interviewed equal proportions of men and women. The percentage of self-identified disadvantaged interviewees varied widely by school (15%-34%). Interview score was a significant predictor of receiving an acceptance offer at all schools (OR range=3.5-13.2). MCAT score and GPA were modestly positively predictive of an acceptance offer at 4 of 5 schools (OR range=3.5-13.2). Disadvantage status predicted offers in 3 of 5 schools. Discussion The consistent strong effect of interview score and the more modest effect GPA in predicting offers suggest that in the context of the overall admissions process, schools are being holistic both within and across institutions in that interview performance, metrics and disadvantage status all contribute to admissions decisions. The role of GPA and MCAT scores are used to screen applicants thus are understated in this study; those with low values generally do not receive an interview. Disadvantage status is associated with a greater likelihood of receiving an offer at some schools but not others, likely reflecting mission-based recruitment practices. Finally, within DA and non-DA interviewees, interview performance remains the strongest and most consistent predictor of receiving an offer, with variable influence of GPA and MCAT score. Subsequent studies will examine interview performance across schools and interview method (traditional versus MMI), relationships with pre-clinical and clinical performance in medical school. 

Speakers
avatar for Benjamin  Chan, M.D., M.B.A.

Benjamin Chan, M.D., M.B.A.

Assistant Dean, Admissions, University of Utah School of Medicine



Sunday February 26, 2017 11:00am - 11:45am
ARCHES

Attendees (19)