Purpose: Socioeconomic disadvantage remains a major determinant of equitable access to, and progression within, medical education. This systematic review and meta-analysis examines both the impact and the magnitude of financial and economic disadvantage on student selection and progression in medical school. Methods: Studies were included if they reported associations between socioeconomic indicators (e.g., parental income, occupation, education, geographic deprivation, or premedical debt) and selection or progression outcomes, and were excluded if they lacked clearly defined economic predictors or sufficient data for binary effect sizes. Searches were conducted across PubMed, Scopus, ERIC, Embase, ProQuest, and EBSCO (2005–2025). Study selection employed an active machine-learning screening process. Extracted data included sample characteristics, socioeconomic measures, and outcome types, with risk of bias assessed using the Risk of Bias Instrument. Random-effects meta-analysis was conducted where appropriate. Results: Thirty-two studies of medical programs were included, yielding 28 effect sizes for selection and 9 for progression. Household economic and educational disadvantage, identified through parental indices, was consistently associated with reduced odds of admission (odds ratio [OR], 0.6; 95% confidence interval [CI], 0.55–0.65) and poorer progression (OR, 0.56; 95% CI, 0.53–0.59). Geographic deprivation also exerted a negative effect, particularly on selection (OR, 0.69; 95% CI, 0.5–0.93). Conclusion Socioeconomic disadvantage exerts a pervasive influence across the medical education continuum. Addressing these inequities requires sustained financial, academic, and psychosocial support both before and during their studies. Students’ economic circumstances should therefore be considered in medical school selection policy and curriculum development to further enhance equity within medical schools and the profession.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Evaluating medical school selection tools is vital for evidence-based student selection. With previous reviews revealing knowledge gaps, this meta-analysis offers insights into the effectiveness of these selection tools. Methods: A systematic review and meta-analysis were conducted applying the following criteria: peer-reviewed articles available in English, published from 2010 and which include empirical data linking performance in selection tools with assessment and dropout outcomes of undergraduate entry medical programs. Systematic reviews, meta-analyses, general opinion pieces, or commentaries were excluded. Effect sizes (ESs) of the predictability of academic and clinical performance within and by the end of the medicine program were extracted, and the pooled ESs were presented. Results: Sixty-seven out of 2,212 articles were included, which yielded 236 ESs. Previous academic achievement predicted medical program academic performance (Cohen’s d=0.697 in early program; 0.619 in end of program) and clinical exams (0.545 in end of program). Within aptitude tests, verbal reasoning and quantitative reasoning predicted academic achievement in the early program and in the last years (0.704 & 0.643, respectively). Overall aptitude tests predicted academic achievement in both the early and last years (0.550 & 0.371, respectively). Neither panel interviews, multiple mini-interviews, nor situational judgement tests (SJT) yielded statistically significant pooled ES. Conclusion Current evidence suggests that learning outcomes are predicted by previous academic achievement and aptitude tests. The predictive value of SJT and topics such as selection algorithms, features of interview (e.g., content of the questions) and the way the interviewers’ reports are used, warrant further research.

Purpose: Evaluating medical school selection tools is vital for evidence-based student selection. With previous reviews revealing knowledge gaps, this meta-analysis offers insights into the effectiveness of these selection tools. Methods: A systematic review and meta-analysis were conducted applying the following criteria: peer-reviewed articles available in English, published from 2010 and which include empirical data linking performance in selection tools with assessment and dropout outcomes of undergraduate entry medical programs. Systematic reviews, meta-analyses, general opinion pieces, or commentaries were excluded. Effect sizes (ESs) of the predictability of academic and clinical performance within and by the end of the medicine program were extracted, and the pooled ESs were presented. Results: Sixty-seven out of 2,212 articles were included, which yielded 236 ESs. Previous academic achievement predicted medical program academic performance (Cohen’s d=0.697 in early program; 0.619 in end of program) and clinical exams (0.545 in end of program). Within aptitude tests, verbal reasoning and quantitative reasoning predicted academic achievement in the early program and in the last years (0.704 & 0.643, respectively). Overall aptitude tests predicted academic achievement in both the early and last years (0.550 & 0.371, respectively). Neither panel interviews, multiple mini-interviews, nor situational judgement tests (SJT) yielded statistically significant pooled ES. Conclusion Current evidence suggests that learning outcomes are predicted by previous academic achievement and aptitude tests. The predictive value of SJT and topics such as selection algorithms, features of interview (e.g., content of the questions) and the way the interviewers’ reports are used, warrant further research.