Home Research Research Library Racial/Ethnic Representation Among American Board of Family Medicine Certification Candidates from 1970 to 2020 Racial/Ethnic Representation Among American Board of Family Medicine Certification Candidates from 1970 to 2020 2022 Author(s) Wang, Ting, O’Neill, Thomas R, Newton, Warren P, Hall, Kennedi, and Eden, Aimee R Topic(s) Family Medicine Certification, and Achieving Health System Goals Volume 35(1):9-17 Source Journal of the American Board of Family Medicine BACKGROUND: Because improved patient outcomes and experiences have been associated with health care workforce diversity, efforts to create a diverse family physician workforce have increased. However, a metric that could properly measure family physician representation in various contexts has seldom been studied. OBJECTIVE: The goal of this study is to propose a new metric logRQ and use it to examine the diversification progress of American Board of Family Medicine (ABFM) certification candidates relative to national, state, and historic populations, as well as medical school matriculants. METHODS: We obtained race/ethnicity for certification candidates from the 2014 to 2020 ABFM Certification Examination Registration questionnaire and examined racial/ethnic representation relative to various populations via logRQs. RESULTS: The total sample comprised 26,368 initial certification candidates and 55,347 continuing certification candidates. Asian, Hispanic, and Black’s logRQ increased by 0.51, 0.42, and 0.41, respectively, in initial certification candidates compared with continuing certification candidates. In addition, logRQ standard deviation ranged from 0.19 to 0.87 across States, indicating state-level variation. Although Black and Hispanic remained underrepresented, the degree of underrepresentation had improved substantially across the past 5 decades, with logRQ increasing from -2.12 (Black) and -1.16 (Hispanic) in the 1970s to -0.46 (Black) and -0.68 (Hispanic) in the 2010s. The race/ethnicity logRQs of 2020 initial certification candidates relative to 2013 to 2014 medical school matriculants were all near 0, reflecting equitable representation. CONCLUSION: We utilized the proposed metric logRQ to quantify the advancement in representation among ABFM certification candidates in different contexts. The proposed logRQ may serve as a useful tool to monitor representation progress systematically. ABFM Research Read all 2019 Physician Opinions about American Board of Family Medicine Self-Assessment Modules (2006–2016) Go to Physician Opinions about American Board of Family Medicine Self-Assessment Modules (2006–2016) 2020 Clinical Quality Measures in a Post-Pandemic World: Measuring What Matters in Family Medicine (ABFM) Go to Clinical Quality Measures in a Post-Pandemic World: Measuring What Matters in Family Medicine (ABFM) 2015 ABFM to Simplify Maintenance of Certification (MOC) for Family Physicians and Make It More Meaningful: A Family Medicine Registry Go to ABFM to Simplify Maintenance of Certification (MOC) for Family Physicians and Make It More Meaningful: A Family Medicine Registry 2024 What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care Go to What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
Author(s) Wang, Ting, O’Neill, Thomas R, Newton, Warren P, Hall, Kennedi, and Eden, Aimee R Topic(s) Family Medicine Certification, and Achieving Health System Goals Volume 35(1):9-17 Source Journal of the American Board of Family Medicine
ABFM Research Read all 2019 Physician Opinions about American Board of Family Medicine Self-Assessment Modules (2006–2016) Go to Physician Opinions about American Board of Family Medicine Self-Assessment Modules (2006–2016) 2020 Clinical Quality Measures in a Post-Pandemic World: Measuring What Matters in Family Medicine (ABFM) Go to Clinical Quality Measures in a Post-Pandemic World: Measuring What Matters in Family Medicine (ABFM) 2015 ABFM to Simplify Maintenance of Certification (MOC) for Family Physicians and Make It More Meaningful: A Family Medicine Registry Go to ABFM to Simplify Maintenance of Certification (MOC) for Family Physicians and Make It More Meaningful: A Family Medicine Registry 2024 What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care Go to What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
2019 Physician Opinions about American Board of Family Medicine Self-Assessment Modules (2006–2016) Go to Physician Opinions about American Board of Family Medicine Self-Assessment Modules (2006–2016)
2020 Clinical Quality Measures in a Post-Pandemic World: Measuring What Matters in Family Medicine (ABFM) Go to Clinical Quality Measures in a Post-Pandemic World: Measuring What Matters in Family Medicine (ABFM)
2015 ABFM to Simplify Maintenance of Certification (MOC) for Family Physicians and Make It More Meaningful: A Family Medicine Registry Go to ABFM to Simplify Maintenance of Certification (MOC) for Family Physicians and Make It More Meaningful: A Family Medicine Registry
2024 What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care Go to What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care