In this study, we investigated the currently applied selective admission criteria and tools of the two-year research master's programs of both the Graduate Schools of Life Sciences and Natural Sciences of Utrecht University (the Netherlands). In addition, we evaluated their transparency to applicants. Both admissions staff members and applicants participated. To determine admission criteria that are important for admission decisions, we ranked 51 admission criteria and, on their basis, combined into six domains: academic background, grades, cognitive ability, research background, personality and personal competencies, motivation factors. To evaluate transparency, we contrasted the perceptions of applicants with the actual importance of admission criteria, as reported by admission staff members. We found that admissions criteria related to personality and personal competencies are less important in admission decisions than criteria related to grades, academic background and motivation. The applicants find the admissions decisions transparent to a moderate degree. This study also revealed that selectors use criteria and tools both with and without predictive value for later graduate performance. Moreover, some of the currently applied admission instruments might be prone to admission biases. We advocate selectors to use admission criteria and tools that are evidence-based, resistant to admission biases, and transparent to the applicants.

Background/introduction As patient populations become more diverse, it is imperative that future physicians receive proper training in order to provide the best quality of care. This study examines medical students' perceptions of how prepared they are in dealing with a diverse population and assesses how included and supported the students felt during their studies. Methods Four semi-structured focus groups were held with medical students across all years of the medical study program of a Dutch university. Focus group transcripts were analyzed thematically. Results Students’ experiences could be categorized as follows: (1) (Minority) identities and personal motivations, (2) Understanding of diversity and an inclusive learning environment, (3) Diversity in education, (4) Experiences of exclusion, (5) Experiences of inclusion, and (6) Lack of awareness. The key findings from the focus groups were that students perceived a lack of diversity and awareness in medical education and were convinced of the need to incorporate diversity to a greater extent and were personally motivated to contribute to incorporating diversity in the curriculum. Students also shared exclusion experiences such as stereotypes and prejudices but also some inclusion experiences such as feelings of belonging. Conclusion Based on our findings, it is recommended that medical schools incorporate diversity education into their curriculum so that health professionals can provide the best quality of care for their diverse patient populations. This education should also ensure that all students feel included in their medical education program.

In the face of increasing and diversifying graduate application numbers, evidencebased selective admissions have become a pressing issue. By conducting multilevel regression analyses on institutional admissions data from a Dutch university, this study aims to determine the predictive value of undergraduate academic indicators for graduate study success on research masters' programs in the life sciences. The results imply that in addition to undergraduate grade point average, undergraduate thesis grade is a valid predictor of graduate grade point average. To a small extent, the examined undergraduate academic indicators also predict graduate degree completion and time to degree. The results from this study can be used by admissions committees for evaluating and improving their current practices of graduate selective admissions.

Signal Detection Theory (SDT) is rarely used in higher education, yet has much potential in informing decision-making. In this methodological paper, we describe the potential of SDT for different higher education contexts and demonstrate its practical application. Both the commonly used regression analyses and SDT analyses provide information on the accuracy of a predictor, and thus which instrument(s) to use. SDT analyses, in addition, provide information on the effects of setting specific cut-off scores on outcomes of interest. SDT provides the sensitivity and specificity information for the chosen instrument(s) at specific cut-off scores (criteria in SDT). This allows for evidence-informed, deliberate choice of cut-off scores to steer toward desired outcomes. Depending on how undesirable false positives and false negatives are considered in a specific situation, a lower or higher cut-off score can be deemed adequate. Using SDT analyses in our example, we demonstrate how to use the results to optimize “real-life” student selection. However, selection is only one of many decision-making practices where SDT is applicable and valuable. We outline some of the areas within higher education decision-making and quality assurance, where SDT can be applied to answer specific questions and optimize decision-making.