Traditional predictors of academic performance in a medical school's independent study program.
OBJECTIVE
To provide predictive information as an initial screening device for admission decisions, generalizable to the population of students opting for the Independent Study Program (ISP) at The Ohio State University College of Medicine.


METHODS
A stepwise multiple-regression technique was used to generate predictor-criterion relationships. A priority code was developed as an applicant screening device. The code is a numeric value based on a combination of applicant grade-point average (GPA) and Medical College Admission Test (MCAT) scores that provides a prediction of first-year performance in medical school. The study sample consisted of the 596 first-year students in the ISP track from 1980 through 1989. The measure of their academic performances was the average score on three National Board of Medical Examiners (NBME) subject ("shelf") examinations in the basic sciences (anatomy, physiology, and biochemistry).


RESULTS
The largest multiple correlations were found between averaged scores on the NBME subject examinations and undergraduate GPAs (R = 34.20; F = 16.79; p = .0001) and scores on the MCAT Biology Knowledge (R = 13.24; F = 47.64; p = .0001), MCAT Chemistry Knowledge (R = 7.86; F = 17.39; p = .0001), and MCAT Skills Analysis: Quantitative (R = 1.39; F = 3.93; p = .0479).


CONCLUSIONS
The predictive value of traditional predictors of preclinical academic performance was established for the nontraditional program at The Ohio State University College of Medicine. Admission officers at other schools may find the priority code helpful as a sorting tool. It may further serve as an "early warning" system for students with marginal academic preparation.
Meleca
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Department of Family Medicine and Internal Medicine, Ohio State University College of Medicine, Columbus.
D003099:College Admission Test; D004504:Education, Medical, Undergraduate; D004521:Educational Measurement; D004522:Educational Status; D011237:Predictive Value of Tests; D016730:Program Development; D012044:Regression Analysis; D013647:Task Performance and Analysis; D014481:United States