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Home Research Research Library The Predictive Validity of the ABFM’s In-Training Examination The Predictive Validity of the ABFM’s In-Training Examination 2015 Author(s) O’Neill, Thomas R, Li, Z, Peabody, Michael R, Lybarger, M, Royal, Kenneth D, and Puffer, James C Topic(s) Education & Training, and Family Medicine Certification Keyword(s) Cognitive Expertise, Graduate Medical Education, In-Training Examination, and Psychometrics Volume Family Medicine Source Family Medicine BACKGROUND AND OBJECTIVES: Our objective was to examine the predictive validity of the American Board of Family Medicine’s (ABFM) In-Training Examination (ITE) with regard to predicting outcomes on the ABFM certification examination. METHODS: This study used a repeated measures design across three levels of medical training (PGY1–PGY2, PGY2–PGY3, and PGY3–initial certification) with three different cohorts (2010–2011, 2011–2012, and 2012–2013) to examine: (1) how well the residents’ ITE scores correlated with their test scores in the following year, (2) what the typical score increase was across training years, and (3) what was the sensitivity, specificity, positive predictive value, and negative predictive value of the PGY3 scores with regard to predicting future results on the MC-FP Examination. RESULTS: ITE scores generally correlate at about .7 with the following year’s ITE or with the following year’s certification examination. The mean growth from PGY1 to PGY2 was 52 points, from PGY2 to PGY3 was 34 points, and from PGY3 to initial certification was 27 points. The sensitivity, specificity, positive predictive value, and negative predictive value were .91, .47, .96, and .27, respectively. CONCLUSION: The ITE is a useful predictor of future ITE and initial certification examination performance. ABFM Research Read all 1999 The item generation methodology of an empiric simulation project Go to The item generation methodology of an empiric simulation project 2010 Family medicine at 40 years of age: the journey to transformation continues Go to Family medicine at 40 years of age: the journey to transformation continues 2021 Family Medicine Residents’ Experience During Early Phases of the COVID-19 Pandemic Go to Family Medicine Residents’ Experience During Early Phases of the COVID-19 Pandemic 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)
Author(s) O’Neill, Thomas R, Li, Z, Peabody, Michael R, Lybarger, M, Royal, Kenneth D, and Puffer, James C Topic(s) Education & Training, and Family Medicine Certification Keyword(s) Cognitive Expertise, Graduate Medical Education, In-Training Examination, and Psychometrics Volume Family Medicine Source Family Medicine
ABFM Research Read all 1999 The item generation methodology of an empiric simulation project Go to The item generation methodology of an empiric simulation project 2010 Family medicine at 40 years of age: the journey to transformation continues Go to Family medicine at 40 years of age: the journey to transformation continues 2021 Family Medicine Residents’ Experience During Early Phases of the COVID-19 Pandemic Go to Family Medicine Residents’ Experience During Early Phases of the COVID-19 Pandemic 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)
1999 The item generation methodology of an empiric simulation project Go to The item generation methodology of an empiric simulation project
2010 Family medicine at 40 years of age: the journey to transformation continues Go to Family medicine at 40 years of age: the journey to transformation continues
2021 Family Medicine Residents’ Experience During Early Phases of the COVID-19 Pandemic Go to Family Medicine Residents’ Experience During Early Phases of the COVID-19 Pandemic
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)