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This paper analyzes eight applicant cohorts matriculating from 2006–2013 and completing residency, focusing on objective variables available in applications. Univariate and multiple regression models test links between these variables and later in-state practice location. 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Ballejos, PhD, MPAa ; Jamie Rierab ; Robert L. Williams, MD, MPH a ; Robert E. Sapién, MD, PhD, MMMc  \nAUTHOR AFFILIATIONS:  \na Department of Family and Community Medicine, University of New Mexico  \nSchool of Medicine, Albuquerque, NMb Undergraduate Medical Education, Office of Evaluation, Education, and Research, University of New Mexico School of Medicine, Albuquerque, NMc Department of Emergency Medicine, University of New Mexico School of Medicine, Albuquerque, NM  \nCORRESPONDING AUTHOR:  \nMarlene P. Ballejos, Department of Family and Community Medicine, University of New Mexico School of Medicine, Albuquerque, NM, [mballejos@salud.unm.edu](mballejos@salud.unm.edu)  \nHOW TO CITE: Ballejos MP, RieraJ, Williams RL, Sapién RE. Objective Admissions Data and In-State Practice:  \nWhat Can We Really Predict?. Fam Med. 2025;57(6):435-438 .  \ndoi: 10.22454/FamMed.2025.503525  \nPUBLISHED: 6 June 2025  \nKEYWORDS: admissions to medical school/residency, career choices, regression analysis  \n© Society of Teachers of Family Medicine  \nABSTRACT  \nBackground and Objectives: Despite the widespread adoption of the holistic review initiative in medical school admissions, admissions committees continue to struggle with identifying applicant attributes that predict future practice location. This challenge has significant practical implications, particularly for institutions committed to addressing regional health care shortages.  \nMethods: We analyzed eight cohorts of applicants who matriculated between 2006 and 2013 and then completed residency. We used univariate and multiple regression models to identify applicant objective attributes linked to later in-state practice. Objective data available in applications included high school location, legal state of residence, Medical College Admissions Test (MCAT) scores, grade point average, college details, graduate school, race/ethnicity, and gender; these were compared by posttraining practice location.  \nResults: Of 509 graduates in practice, 212 (41.7%) were in-state. In-state graduates were older (P\u003C.001), predominately underrepresented in medicine (42% vs 33%, P\u003C.01), graduated from urban high schools (28% vs 18%, P\u003C.05), attended in-state colleges (66% vs 55%, P\u003C.05), and had lower MCAT scores (56th vs 67th percentile, P\u003C.001) . Regression analysis revealed that only age, urban high school, and MCAT score significantly contributed to the model (R2 =0.064, P =.004) .  \nConclusions: This study shows the limited value of applicant objective data to admissions committees in predicting future in-state practice. The findings emphasize the importance of a holistic admissions process and the value of considering subjective applicant data in efforts to predict future practice location. Additional research may help identify which subjective aspects of an applicant can assist admissions committees in this effort.  \nINTRODUCTION  \nMedical school admissions committees commonly struggle to identify which applicants are most likely to practice instate following training. This struggle is of particular interest for institutions committed to addressing regional health care shortages and for specialties that serve in areas of health care shortage, such as family medicine. Although research exists on predicting academic performance in medical school applicants using cognitive and noncognitive variables, 1–4 research concerning applicant variables affecting future practice location is sparse.  \nStudies have reported that residency training location is a stronger predictor than medical school of future practice location.5–8 Reportedly, 52.9% of residents practice in the state of residency training. 9 Another study reported that ties to the state and primary care residency are associated with instate practice. 10 Pipeline programs also play a role in shaping  \ngraduates’ career choices and increasing the likeliho","cbCaiqIvhuktHAt5","https://ap.wps.com/l/cbCaiqIvhuktHAt5","pdf",183481,"English","# Abstract\n## Background and Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Rationale and Existing Evidence\n## University of New Mexico School of Medicine Context\n# Methods\n## Study Design and Dataset\n## Data Sources","[{\"question\":\"What problem does the study address in medical school admissions?\",\"answer\":\"Admissions committees struggle to identify applicant attributes that predict where future practice will occur, especially in-state practice. The paper frames this as important for institutions addressing regional health care shortages.\"},{\"question\":\"Which objective applicant data are analyzed?\",\"answer\":\"The study evaluates high school location, legal state of residence, MCAT scores, GPA, college and graduate school details, race/ethnicity, and gender. These variables are compared against later practice location.\"},{\"question\":\"What do the regression results show about predicting in-state practice?\",\"answer\":\"Regression analysis indicates that only age, urban high school, and MCAT score significantly contribute to the model. The overall explanatory power is limited (R2 = 0.064).\"},{\"question\":\"What conclusion does the study draw about using objective data for admissions decisions?\",\"answer\":\"Objective admissions data have limited value for predicting future in-state practice. The findings support holistic review and suggest that subjective applicant information may be important for improving prediction.\"}]","Objective Admissions Data and In-State Practice: What Can We Really Predict? - Brief Report | PDF",1788538334]