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Selecting top candidates for medical school selection interviews- a non-compensatory approach

机译:选择医学学校选择访谈的顶级候选人 - 一种非补偿方法

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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.
机译:医学院应用一系列选择方法,以确保入住的学生在该计划中取得成功。在澳大利亚,选择工具通常包括学术成果的措施(例如,澳大利亚第三次录取等级 - Atar)和能力测试(例如本科医学和健康科学招生测试 - UMAT)。这些最常用于确定哪些申请人被邀请进行额外选择流程,例如访谈。但是,之前没有检查选择过程第一部分的疗效。特别是,补偿或非补偿方法在评估认知和耐受性测试的结果方面更有效,它们会影响所选学生的人口统计?本研究利用了在2013年至2018年期间申请进入联合国医学计划的主流国内学生的连续队列的数据。与非补偿排名模型进行了赔偿排名选择模型。最初,每个申请人的ATAR标记和UMAT分数在每个群组内排名。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的最低等级确定了每个申请人的结果。在所有队列中,评估了每个模型对选定面谈的申请人的性别和社会经济地位的影响。非补偿排名选择模型导致了对面试邀请的基本上更高的Atar和Umat阈值,对所选申请人的社会经济地位没有显着影响。这些结果非常重要,表明可以提高学术门槛,以便在低社会经济背景对申请人对申请人进行任何负面影响。总体而言,本研究中聚集的证据表明,非补偿模型对于选择医学学生选择访谈的申请人来说是优选的。

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