Predictive validity of progress test scores and academic achievement on SMLE performance among Saudi medical graduates

进度测试分数和学业成绩对沙特阿拉伯医学毕业生SMLE考试成绩的预测效度

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Abstract

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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