More than a score: Evaluating military Veterans' success when applying to medical school

Abstract: Due to the inherent nature of service in both military and medical careers, some veterans are drawn to medicine after completing military service. However, there are significant financial and academic barriers for veterans applying into medical fields. Average grade point average (GPA) and Medical College Admission Test (MCAT) score are two heavily weighted metrics in the medical school application process. Veterans often have less rigorous academic backgrounds and more limited preparation for the MCAT in comparison to traditional medical school applicants. As a result, veterans may be less competitive than traditional applicants through direct comparisons of GPAs and MCAT scor s. The authors' analysis focuses on whether this limitation affected veterans' success in applying to medical school. Using aggregated data from the American Association of Medical Colleges (AAMC), the authors analyzed the average GPAs and MCAT scores of applicants with any military experience (defined as "military applicants") compared to the pool of all applicants from 2018 to 2024. During this period, military applicants to U.S. MD programs had an average GPA that was 0.16 points lower and average MCAT score 3.4 points below the average of all applicants. Despite lower academic metrics, the military applicant acceptance rate to MD programs was 41.7% compared to 40.7% for all applicants. Veterans should not be deterred from seeking a path of service in medicine by below average GPAs or MCAT scores. Medical school admissions over the past 6 years show that medical school admission committees value the perspective, life experience, and skills military veterans bring to medicine despite their lower GPAs and MCAT scores.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.