Office of Medical Education, the University of New South Wales, UNSW, Sydney, NSW, 2052, Australia.
BMC Med Educ. 2020 Apr 15;20(1):113. doi: 10.1186/s12909-020-02031-6.
Medical schools apply a range of selection methods to ensure that admitted students succeed in the program. In Australia, selection tools typically include measures of academic achievement (e.g. the Australian Tertiary Admission Rank - ATAR) and aptitude tests (e.g. the Undergraduate Medicine and Health Sciences Admissions Test - UMAT). These are most commonly used to determine which applicants are invited for additional selection processes, such as interviews. However, no previous study has examined the efficacy of the first part of the selection process. In particular, are compensatory or non-compensatory approaches more effective in evaluating the outcomes of cognitive and aptitude tests, and do they affect the demographics of students selected for interview?
This study utilised data from consecutive cohorts of mainstream domestic students who applied to enter the UNSW Medicine program between 2013 to 2018. A compensatory ranked selection model was compared with a non-compensatory ranked model. Initially, ATAR marks and UMAT scores for each applicant were ranked within each cohort. In the compensatory model, the mean of the ATAR and UMAT ranks were used to determine the outcome. In the non-compensatory model, the lowest rank of ATAR and UMAT determined the outcome for each applicant. The impact of each model on the gender and socioeconomic status of applicants selected to interview was evaluated across all cohorts.
The non-compensatory ranked selection model resulted in substantially higher ATAR and UMAT thresholds for invitation to interview, with no significant effect on the socioeconomic status of the selected applicants.
These results are important, demonstrating that it is possible to raise the academic threshold for selection to medicine without having any negative impact on applicants from low socioeconomic backgrounds. Overall, the evidence gathered in this study suggests that a non-compensatory model is preferable for selecting applicants for medical student selection interview.
医学院校采用多种选拔方法,确保录取的学生能够成功完成学业。在澳大利亚,选拔工具通常包括学业成绩(如澳大利亚高等教育入学排名-ATAR)和能力测试(如本科医学和健康科学入学考试-UMAT)。这些通常用于确定哪些申请人有资格参加额外的选拔程序,如面试。然而,以前没有研究检验选拔过程第一部分的效果。特别是,补偿或非补偿方法在评估认知和能力测试的结果方面更有效,并且它们是否会影响面试学生的人口统计学特征?
本研究使用了 2013 年至 2018 年间连续几批申请进入新南威尔士大学医学专业的主流国内学生的数据。比较了补偿性排名选择模型和非补偿性排名模型。最初,在每个队列中对每个申请人的 ATAR 分数和 UMAT 分数进行排名。在补偿模型中,使用 ATAR 和 UMAT 等级的平均值来确定结果。在非补偿模型中,ATAR 和 UMAT 的最低等级决定了每个申请人的结果。在所有队列中评估了每种模型对入选面试的申请人的性别和社会经济地位的影响。
非补偿性排名选择模型导致邀请面试的 ATAR 和 UMAT 门槛大幅提高,而对入选申请人的社会经济地位没有显著影响。
这些结果很重要,表明在对医学选拔没有任何负面影响的情况下,有可能提高选拔的学术门槛。总的来说,本研究收集的证据表明,非补偿性模型更适合选择医学专业学生面试的申请人。