Àá½Ã¸¸ ±â´Ù·Á ÁÖ¼¼¿ä. ·ÎµùÁßÀÔ´Ï´Ù.
KMID : 1038320220190010031
º¸°ÇÀÇ·á±³À°Æò°¡
2022 Volume.19 No. 1 p.31 ~ p.31
Medical student selection process enhanced by improving selection algorithms and changing the focus of interviews in Australia: a descriptive study
Shulruf Boaz

Velan Gary Mayer
Kennedy Sean Edward
Abstract
Purpose: The study investigates the efficacy of new features introduced to the selection process for medical school at the University of New South Wales, Australia: (1) considering the relative ranks rather than scores of the Undergraduate Medicine and Health Sciences Admission Test and Australian Tertiary Admission Rank; (2) structured interview focusing on interpersonal interaction and concerns should the applicants become students; and (3) embracing interviewers¡¯ diverse perspectives.

Methods: Data from 5 cohorts of students were analyzed, comparing outcomes of the second year in the medicine program of 4 cohorts of the old selection process and 1 of the new process. The main analysis comprised multiple linear regression models for predicting academic, clinical, and professional outcomes, by section tools and demographic variables.

Results: Selection interview marks from the new interview (512 applicants, 2 interviewers each) were analyzed for inter-rater reliability, which identified a high level of agreement (kappa=0.639). No such analysis was possible for the old interview since it required interviewers to reach a consensus. Multivariate linear regression models utilizing outcomes for 5 cohorts (N=905) revealed that the new selection process was much more effective in predicting academic and clinical achievement in the program (R2=9.4%?17.8% vs. R2=1.5%?8.4%).

Conclusion: The results suggest that the medical student selection process can be significantly enhanced by employing a non-compensatory selection algorithm; and using a structured interview focusing on interpersonal interaction and concerns should the applicants become students; as well as embracing interviewers¡¯ diverse perspectives.
KEYWORD
Algorithms, Consensus, Linear models, Medical schools, School admission criteria
FullTexts / Linksout information
Listed journal information
MEDLINE ÇмúÁøÈïÀç´Ü(KCI) KoreaMed