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: 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.

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.