Home Research Research Library Family Medicine Residents’ Debt and Certification Examination Performance Family Medicine Residents’ Debt and Certification Examination Performance 2019 Author(s) Phillips, Julie P, Peterson, Lars E, Kovar-Gough, Iris, O’Neill, Thomas R, Peabody, Michael R, and Phillips, Robert L Topic(s) Education & Training, and Family Medicine Certification Keyword(s) Visiting Scholar/Fellow Volume PRiMER Source PRiMER Introduction: Previous studies have found that medical students and internal medicine residents with high educational debt perform less well on examinations. The purpose of this study was to examine the relationship between educational debt and family medicine residents’ performance on initial in-training and board certification examinations. Methods: Our study was a cross-sectional secondary analysis of American Board of Family Medicine (ABFM) data collected from residents (N=5,828) who registered for the Family Medicine Certification Examination (FMCE) in 2014 and 2015, representing 85.8% of graduating family medicine residents in the United States in those years. Multivariable linear and logistic regression modeling was used to examine the relationship between debt level and examination scores, and also to explore the relationship between debt level and passing the initial FMCE. Results: After controlling for demographic variables, residents with high debt ($150,000 to $249,999) and very high debt (more than $250,000) performed significantly worse than those with no debt on the initial in-training examination (score differences of 14.2 [CI 8.6, 19.8] and 15.8 [CI 10.3, 21.4] points, respectively) and FMCE (score differences of 19.3 points [CI 13.4, 25.3] and 30.4 points [CI 24.6, 36.3], respectively). Additionally, those with debt above $250,000 had half the odds of passing their initial FMCE (OR 0.45; CI 0.27-0.75). Conclusions: High educational debt is associated with lower examination performance among family medicine residents. This may be because residents with more debt have more stress or fewer day-to-day financial resources. However, confounding factors may also contribute to this association. ABFM Research Read all 2022 Residency Learning Networks: Why and How. Go to Residency Learning Networks: Why and How. 2024 The Gender Wage Gap Among Early-Career Family Physicians Go to The Gender Wage Gap Among Early-Career Family Physicians 2021 Maternity Care Tracks at US Family Medicine Residency Programs Go to Maternity Care Tracks at US Family Medicine Residency Programs 2025 Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers Go to Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers
Author(s) Phillips, Julie P, Peterson, Lars E, Kovar-Gough, Iris, O’Neill, Thomas R, Peabody, Michael R, and Phillips, Robert L Topic(s) Education & Training, and Family Medicine Certification Keyword(s) Visiting Scholar/Fellow Volume PRiMER Source PRiMER
ABFM Research Read all 2022 Residency Learning Networks: Why and How. Go to Residency Learning Networks: Why and How. 2024 The Gender Wage Gap Among Early-Career Family Physicians Go to The Gender Wage Gap Among Early-Career Family Physicians 2021 Maternity Care Tracks at US Family Medicine Residency Programs Go to Maternity Care Tracks at US Family Medicine Residency Programs 2025 Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers Go to Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers
2024 The Gender Wage Gap Among Early-Career Family Physicians Go to The Gender Wage Gap Among Early-Career Family Physicians
2021 Maternity Care Tracks at US Family Medicine Residency Programs Go to Maternity Care Tracks at US Family Medicine Residency Programs
2025 Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers Go to Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers