Correlating selection criteria with subsequent performance as residents.
The objective of this study was to determine which criteria in the residency application had the highest correlation with subsequent performance of orthopaedic residents. Data collected from the application files of 58 residents included scores on standardized tests, number of honors grades in the basic and clinical years of medical school, election to Alpha Omega Alpha, numbers of research projects and publications, and numbers of extracurricular activities. Measures of performance included scores on the Orthopaedic In-Training Examination and American Board of Orthopaedic Surgery Part I Examination, and faculty evaluations of overall, cognitive, affective, and psychomotor performance. The number of honors grades on clinical rotations was the strongest predictor of performance, whereas election to Alpha Omega Alpha was second. The only other significant correlation was between the number of fine motor activities and psychomotor performance. None of the predictor variables had a significant correlation with Orthopaedic In-Training Examination or American Board of Orthopaedic Surgery Examination scores. Consistency between faculty rankings in each of the four categories was supported by regression analysis. From the results of this study, it appears that academic performance in clinical clerkships in medical school is the most predictive of resident performance. Range restriction in the data available for orthopaedic residency applicants, however, likely precludes the development of a reliable model to assist in the selection of orthopaedic residents.
Dirschl
Douglas R
DR
University of North Carolina School of Medicine, Wake Medical Education Institute, Raleigh, NC, USA.
Dahners
Laurence E
LE
Adams
George L
GL
Crouch
John H
JH
Wilson
Frank C
FC
D002983:Clinical Competence; D004503:Education, Medical, Graduate; D004521:Educational Measurement / Q000379:methods*; D007396:Internship and Residency; D009985:Orthopedics / Q000193:education; D010560:Personnel Selection / Q000379:methods*; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D014481:United States; D000078329:Workforce