Forecasting veterinary school admission probabilities for undergraduate student profiles

Predictive Value of Veterinary Student Application Data for Class Rank at End of Year 1

Student applications for admission to the University of Georgia College of Veterinary Medicine include the following information: undergraduate grade point average (GPA), GPA in science courses (GPAScience), GPA for the last 45 credit hours (GPALast45hrs), results for the Graduate Record Examination Quantitative and Verbal Reasoning Measures (GRE-QV), results for the GRE Analytical Writing Measure (GRE-AW), and grades received for 10 required prerequisite courses. In addition, three faculty members independently review and score subjective information in applicants’ files (FileScore). The admissions committee determines a composite Admission Score (AdmScore), which is based on GPA, GPAScience, GPALast45hrs, GRE-QV, GRE-AW, and the FileScore. The AdmScore is generally perceived to be a good predictor of class rank at the end of year 1 (CREY1). However, this has not been verified, nor has it been determined which components of the AdmScore have the strongest correlation with CREY1. The present study therefore compared each component of the AdmScore for correlation with CREY1, for the three classes admitted in 2015, 2016 and 2017 (Class15, Class16, Class17). Results suggest that only a few components of the application file are needed to make strong predictive statements about the academic success of veterinary students during the first year of the curriculum.

Student selection to a rural veterinary school. 2: predictors of student performance and attrition

OBJECTIVE

To evaluate the extent to which current selection processes at Charles Sturt University, New South Wales, Australia, predict performance within and attrition from the veterinary degree program.

DESIGN

Retrospective evaluation of application details and student performance data.

METHODS

Database records of 424 students entering the veterinary program were retrieved from university records, including all students graduating (n = 356) or leaving the program without completing (n = 68) between 2005 and 2016. Demographic data were related to results of selection processes and achievement within the degree using univariable and multivariable general linear and logistic regression analyses.

RESULTS

The grade point average achieved over the 6 years of the degree was influenced by academic achievement prior to entry, gender (females performed ≈ 2% better than males) and interview scores. Preceptor evaluation of final-year clinical performance was associated with interview scores, gender (males performed ≈ 4% better than females) and residential address at the time of application. Attrition for personal reasons was more common for female students and students who had completed a prior degree, and students who experienced academic or personal attrition had lower written application scores. Factors that have been previously linked to poor academic outcomes, including rural or low socioeconomic background, were not associated with adverse student outcomes.

CONCLUSION

Selection processes did not systematically disadvantage students admitted to the degree program. Gender differences in personal attrition and academic and clinical performance warrant further evaluation. Factors in addition to academic aptitude predicted student success.