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Item Impact of parallel planning on residency match rate success(Springer Nature, 2025-03-19) Rusk, Debra; Holt, Ashley; Harvey, Brianna; Shanks, Anthony L.Background: Medical students targeting competitive specialties or those with lower competitiveness for their preferred specialty are encouraged to parallel apply to a less competitive field. The AAMC provides data on the number of applicants who parallel apply but little information exists on their match success. Objective: Our objective is to describe the success rates for students who parallel apply to more than one specialty. Methods: Following IRB exemption, a retrospective cohort study of Indiana University School of Medicine graduates from the 2021–2024 residency match cycles was conducted. ERAS data and match reports were reviewed to identify students who parallel applied to more than one specialty, determining their match outcomes. Subgroup analyses were performed based on specialty type, and descriptive statistics were reported. Results: Between 2021 and 2024, 1,411 IUSM students applied for the match, with 225 (16%) having a parallel plan; 39% of these students matched into their preferred specialty, 56% into their parallel specialty, and 5% did not match. The most common parallel plan specialties were Anesthesiology, Orthopaedic Surgery, and OBGYN. There were no statistically significant differences in parallel application rates among surgical, hospital-based, and primary care specialties. Conclusions: Our study shows that 1 in 6 students will apply to a parallel specialty, with more than half matching into their parallel plan, making it a viable strategy for those targeting competitive specialties or with lower competitiveness. We found no difference in application rates between surgical, hospital-based, and primary care specialties, emphasizing the need for individualized competitiveness guidance.Item Impact of a USMLE Step 2 Prediction Model on Medical Student Motivations(Sage, 2025-02-18) Shanks, Anthony L.; Steckler, Ben; Smith, Sarah; Rusk, Debra; Walvoord, Emily; Dafoe, Erin; Wallach, PaulPURPOSE: With the transition of USMLE Step 1 to Pass/Fail, Step 2 CK carries added weight in the residency selection process. Our goal was to develop a Step 2 predicted score to provide to students earlier in medical school to assist with career mentoring. We also sought to understand how the predicted scores affected student’s plans. METHOD: Traditional statistical models and machine learning algorithms to identify predictors of Step 2 CK performance were utilized. Predicted scores were provided to all students in the Class of 2024 at a large allopathic medical school. A cross-sectional survey was conducted to assess if the estimated score in uenced career or study plans. RESULTS: The independent variables that resulted in the most predictive model included CBSE score, Organ System course exam scores and Phase 2 (Third Year Clinical Clerkships) NBME percentile scores (Step2CK= 191.984 + 0.42 (CBSE score) + 0.294 (Organ Systems) + 0.409 (Average NBME). The standard error of the prediction model was 7.6 with better accuracy for predicted scores greater than 230 (SE 8.1) as compared to less than 230 (SE 12.8). Nineteen percent of respondents changed their study plan based on the predicted score result. Themes identified from the predicted score included reassurance for career planning and the creation of anxiety and stress. CONCLUSION: A Step 2 Predicted Score, created from pre-existing metrics, was a good estimator of Step 2 CK performance. Given the timing of Step 2 CK, a predicted score would be a useful tool to counsel students during the specialty and residency selection process.Item Recruiting to Regional Campuses: Using Scholarly Concentrations Model Across Eight Campuses at Indiana University School of Medicine(2022-04) Birnbaum, Deborah R.; Wallach, Paul M.; Rojas, MichelleItem Assessing a Longitudinal Educational Experience for Continuous Quality Improvement(2022-11) Birnbaum, Deborah R.; Masseria, Anthony; Walsh, Sarah; Rojas, MichelleItem Assessing a Longitudinal Educational Experience for Continuous Quality Improvement(2023-06) Birnbaum, Deborah R.; Masseria, Anthony; Walsh, Sarah