ObjectivesTo examine the predictive validity of pre-admission variables on students’ performance in a medical school in Saudi Arabia. MethodsIn this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis.ResultsIn preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students’ progress test performance (p<0.001 and B=19.02).ConclusionsOnly the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years.

Introduction: Students with varying personality traits are likely to employ diverse learning and study strategies. However, this relationship was never explored before in the medical education context. This study's aim was to explore the relationship between learning strategies, and personality traits among medical students. Methods: This study was a cross-sectional study, and a quantitative approach was employed using two self-administered questionnaires: one to assess the personality traits from the five-factor model (Conscientiousness, Neuroticism, Extraversion, Openness and Agreeableness), and the other to assess ten learning strategies (Anxiety, Attitude, Concentration, Information Processing, Motivation, Selecting Main Ideas, Self-Testing, Test Strategies, Time Management and Using Academic Resources). A stratified random sampling technique was used to recruit medical students at Alfaisal University in the preclinical and clinical years (N=309). Pearson correlation coefficient was used to measure the relationship between variables, and linear regression was used to evaluate how personality traits predicted learning strategies selection. Results: Personality traits predicted the selection of learning strategies, especially Conscientiousness and Neuroticism. Conscientiousness showed a positive correlation with seven learning strategies and was the most important predictor of learning strategies students employ. Neuroticism correlations and predictions were negative. The other three traits showed weaker correlations. These correlations were between Extraversion and Using Academic Resources (r=0.27), Information Processing (r= 0.23) and Attitude (r=0.19), Openness and Information Processing (r=0.29), and Agreeableness and Attitude (r=0.29). Conclusion: All Personality domains influence at least one learning strategy, especially Conscientiousness and Neuroticism. This study helps built a foundation for individualized coaching and mentorship in medical education.