Phoenix Newsletter - October 2025 President’s Message: Enduring Commitments in a Time of Change Read President’s Message: Enduring Commitments in a Time of Change
Home Research Research Library Response: Re: Burnout in Young Family Physicians: Variation Across States Response: Re: Burnout in Young Family Physicians: Variation Across States 2018 Author(s) Hansen, A, Hansen, Elizabeth Rose, and Peterson, Lars E Topic(s) Achieving Health System Goals Keyword(s) National Graduate Survey, Physician Experience (Burnout / Satisfaction), and Visiting Scholar/Fellow Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine Dr. Kendall brings up valuable considerations as we seek to understand the personal and institutional factors influencing physician burnout. As Dr. Mack discussed in his commentary1, family physicians experience burnout at rates that are higher than average for many reasons, including less time spent in direct patient care, more administrative burdens, and more work hours.2 Relevant policies and culture in family medicine and health care vary at the state level, allowing states to function as real-life laboratories; understanding state-level variation can help us to identify and remedy the underlying causes of burnout. Dr. Kendall offers 3 major criticisms of our study that are largely beyond the scope of our study but suggest avenues for future research. ABFM Research Read all 2016 Reimagining Our Relationships with Patients: A Perspective from the Keystone IV Conference Go to Reimagining Our Relationships with Patients: A Perspective from the Keystone IV Conference 2018 Physician Perceptions of Performance Feedback in a Quality Improvement Activity Go to Physician Perceptions of Performance Feedback in a Quality Improvement Activity 2020 Asthma Care Quality, Language, and Ethnicity in a Multi-State Network of Low-Income Children Go to Asthma Care Quality, Language, and Ethnicity in a Multi-State Network of Low-Income Children 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) Hansen, A, Hansen, Elizabeth Rose, and Peterson, Lars E Topic(s) Achieving Health System Goals Keyword(s) National Graduate Survey, Physician Experience (Burnout / Satisfaction), and Visiting Scholar/Fellow Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2016 Reimagining Our Relationships with Patients: A Perspective from the Keystone IV Conference Go to Reimagining Our Relationships with Patients: A Perspective from the Keystone IV Conference 2018 Physician Perceptions of Performance Feedback in a Quality Improvement Activity Go to Physician Perceptions of Performance Feedback in a Quality Improvement Activity 2020 Asthma Care Quality, Language, and Ethnicity in a Multi-State Network of Low-Income Children Go to Asthma Care Quality, Language, and Ethnicity in a Multi-State Network of Low-Income Children 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
2016 Reimagining Our Relationships with Patients: A Perspective from the Keystone IV Conference Go to Reimagining Our Relationships with Patients: A Perspective from the Keystone IV Conference
2018 Physician Perceptions of Performance Feedback in a Quality Improvement Activity Go to Physician Perceptions of Performance Feedback in a Quality Improvement Activity
2020 Asthma Care Quality, Language, and Ethnicity in a Multi-State Network of Low-Income Children Go to Asthma Care Quality, Language, and Ethnicity in a Multi-State Network of Low-Income Children
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
