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Home Research Research Library A Match Made in Rural: Interpreting Match Rates and Exploring Best Practices A Match Made in Rural: Interpreting Match Rates and Exploring Best Practices 2023 Author(s) Longenecker, Randall, Oster, Natalia V, Peterson, Lars E, Andrilla, C Holly A, Schmitz, David F, Evans, David V, Morgan, Zachary J, Pollack, Samantha W, and Patterson, Davis G Keyword(s) Rural Volume Family Medicine Source Family Medicine Methods: Using a published listing of rural programs, 25 years of National Resident Matching Program data, and 11 years of American Osteopathic Association match data, this study (1) documents patterns in initial match rates for rural versus urban residency programs, (2) compares rural residency match rates with program characteristics for match years 2009-2013, (3) examines the association of match rates with program outcomes for graduates in years 2013-2015, and (4) explores recruitment strategies using residency coordinator interviews. Results: Despite increases in positions offered over 25 years, the fill rates for rural programs have improved relative to urban programs. Small rural programs had lower match rates relative to urban programs, but no other program or community characteristics were predictors of match rate. Match rates were not indicative of any of five measures of program quality nor of any single recruiting strategy. Conclusions: Understanding the intricacies of rural residency inputs and outcomes is key to addressing rural workforce gaps. Match rates likely reflect challenges of rural workforce recruitment generally and should not be conflated with program quality. ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
Author(s) Longenecker, Randall, Oster, Natalia V, Peterson, Lars E, Andrilla, C Holly A, Schmitz, David F, Evans, David V, Morgan, Zachary J, Pollack, Samantha W, and Patterson, Davis G Keyword(s) Rural Volume Family Medicine Source Family Medicine
ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare
2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence
2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality
2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone