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Home Research Research Library Prevalence and Predictors of Burnout Among Resident Family Physicians Prevalence and Predictors of Burnout Among Resident Family Physicians 2024 Author(s) Doe, Sydney, Coutinho, Anastasia J, Weidner, Amanda K H, Cheng, Yue, Sanders, Kaplan, Bazemore, Andrew W, Phillips, Robert L, and Peterson, Lars E Topic(s) Education & Training, and Achieving Health System Goals Keyword(s) Graduate Medical Education, Physician Experience (Burnout / Satisfaction), and Visiting Scholar/Fellow Volume Family Medicine Source Family Medicine Background and Objectives: Resident burnout may affect career choices and empathy. We examined predictors of burnout among family medicine residents. Methods: We used data from the 2019–2021 American Board of Family Medicine Initial Certification Questionnaire, which is required of graduating residents. Burnout was a binary variable defined as reporting callousness or emotional exhaustion once a week or more. We evaluated associations using bivariate and multilevel multivariable regression analyses. Results: Among 11,570 residents, 36.4% (n=4,211) reported burnout. This prevalence did not significantly vary from 2019 to 2021 and was not significantly attributable to the residency program (ICC=0.07). Residents identifying as female reported higher rates of burnout (39.0% vs 33.4%, AOR=1.29 [95% CI 1.19–1.40]). Residents reporting Asian race (30.5%, AOR=0.78 [95% CI 0.70–0.86]) and Black race (32.3%, AOR=0.71 [95% CI 0.60–0.86]) reported lower odds of burnout than residents reporting White race (39.2%). We observed lower rates among international medical graduates (26.7% vs 40.3%, AOR=0.54 [95% CI 0.48–0.60]), those planning to provide outpatient continuity care (36.0% vs 38.7%, AOR=0.77 [95% CI 0.68–0.86]), and those at smaller programs (31.7% for <6 residents per class vs 36.3% for 6–10 per class vs 40.2% for >10 per class). Educational debt greater than $250,000 was associated with higher odds of burnout than no debt (AOR=1.29 [95% CI 1.15–1.45]). Conclusions: More than one-third of recent family medicine residents reported burnout. Odds of burnout varied significantly with resident and program characteristics. ABFM Research Read all 2019 PURSUING PRACTICAL PROFESSIONALISM: FORM FOLLOWS FUNCTION Go to PURSUING PRACTICAL PROFESSIONALISM: FORM FOLLOWS FUNCTION 2020 Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Go to Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) 2020 Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative Go to Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative 2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
Author(s) Doe, Sydney, Coutinho, Anastasia J, Weidner, Amanda K H, Cheng, Yue, Sanders, Kaplan, Bazemore, Andrew W, Phillips, Robert L, and Peterson, Lars E Topic(s) Education & Training, and Achieving Health System Goals Keyword(s) Graduate Medical Education, Physician Experience (Burnout / Satisfaction), and Visiting Scholar/Fellow Volume Family Medicine Source Family Medicine
ABFM Research Read all 2019 PURSUING PRACTICAL PROFESSIONALISM: FORM FOLLOWS FUNCTION Go to PURSUING PRACTICAL PROFESSIONALISM: FORM FOLLOWS FUNCTION 2020 Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Go to Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) 2020 Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative Go to Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative 2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
2019 PURSUING PRACTICAL PROFESSIONALISM: FORM FOLLOWS FUNCTION Go to PURSUING PRACTICAL PROFESSIONALISM: FORM FOLLOWS FUNCTION
2020 Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Go to Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE)
2020 Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative Go to Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative
2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
