Diplomate Spotlight Opening Doors with Board Certification: A Conversation with Long Standing Diplomate Joseph Cook Read Opening Doors with Board Certification: A Conversation with Long Standing Diplomate Joseph Cook
Phoenix Newsletter - July 2025 Available Now: 2026 5-Year Cycle Registration Read Available Now: 2026 5-Year Cycle Registration
Home Research Research Library The reliability of American Board of Family Medicine examinations: implications for test takers The reliability of American Board of Family Medicine examinations: implications for test takers 2012 Author(s) Royal, Kenneth D, and Puffer, James C Keyword(s) Psychometrics Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine A common theme among family physicians who have performed poorly repeatedly on the American Board of Family Medicine (ABFM) Maintenance of Certification (MC-FP) examination is the complaint that they received a score that was identical, or almost identical, to their score on a previous administration of the examination. From their perspective, why they received the exact same score (or a very similar score), despite additional study time and preparation, is a mystery. Often, physicians assume a mix-up has occurred and ask if it is possible that results have been provided erroneously from their previous attempt. After a psychometric review, it is clear that there is no mistake at all. In fact, we anticipate many test takers will receive a comparable score on future successful attempts at taking the examination. We base this anticipation on the psychometric concept of reliability. 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) Royal, Kenneth D, and Puffer, James C Keyword(s) Psychometrics Volume Journal of the American Board of Family Medicine Source Journal of the American Board of 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