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Predictive validity of progress test scores and academic achievement on SMLE performance among Saudi medical
1Head of Medical Education Department, Associate Professor of Medical Education, College of Medicine, Qassim University, Qassim, KSA.
Introduction:
Progress testing (PT) has been increasingly adopted in KSA medical education as a longitudinal assessment strategy. However, limited research has explored its predictive validity concerning high-stakes licensing examinations such as the Saudi Medical Licensing Examination (SMLE). This study determined whether final-year Saudi Progress Test (SPT) scores, alongside cumulative grade point average (cGPA), predict SMLE performance among medical graduates.
Material And Methods:
This retrospective cohort study included 820 medical graduates from a single Saudi medical school. Students' cumulative grade point average (cGPA), final-year SPT scores, and SMLE results were analyzed. Pearson's and Spearman's correlation coefficients assessed associations between variables, whereas ANOVA with post-hoc testing compared outcomes across cGPA categories. The relationship between SPT performance bands and the number of SMLE attempts was also explored.
Results:
SPT scores demonstrated a moderate positive correlation with SMLE performance (r = 0.415, p < 0.001), whereas cGPA showed a stronger correlation (r = 0.605, p < 0.001). Discipline-specific correlations between SPT and SMLE scores ranged from r = 0.175 (Pediatrics) to r = 0.330 (Internal Medicine). Students scoring below 30 % on the SPT were significantly more likely to require multiple SMLE attempts, suggesting a potential early-risk threshold.
Conclusion:
Final-year SPT performance moderately predicts SMLE outcomes and provides insights complementary to cGPA. Progress testing may serve as a formative tool to identify at-risk students and support targeted interventions, thereby enhancing readiness for licensure. As this was a single-center study, the findings should be interpreted with caution. Future research should validate results across multiple institutions and explore longitudinal predictive models.
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