Table 1_Integrating iHEC into standardized residency training for chronic disease management: a structural equation analysis of competency outcomes via digital health platforms.docx

Structural equation modeling (SEM) with bias-corrected bootstrapping was performed to evaluate direct and indirect associations.</p>Results<p>Baseline characteristics and educational assessments were comparable between cohorts. After adjustment for baseline performance, specialty, and calendar time, the iHEC cohort demonstrated higher post-rotation OSCE scores [adjusted mean difference (AMD) 6.85, 95% CI 5.12–8.58, p < 0.001], knowledge scores (AMD 5.42, 95% CI 3.84–7.00, p < 0.001), SDL scores (AMD 9.86, 95% CI 7.21–12.51, p < 0.001), and critical thinking scores (AMD 12.45, 95% CI 9.15–15.75, p < 0.001).

The respecified SEM demonstrated good model fit (CFI = 0.962, TLI = 0.951, RMSEA = 0.052, SRMR = 0.038). The iHEC teaching model was positively associated with SDL (β = 0.68, p < 0.001) and educational performance (direct effect β = 0.36, p < 0.001). SDL showed a significant indirect association with educational performance (β = 0.38, 95% bootstrap CI 0.21–0.55), accounting for 51.4% of the total association.</p>Conclusion<p>The iHEC hypertension teaching model was associated with improved resident educational outcomes, with enhanced self-directed learning representing a potential explanatory pathway underlying these observed associations; prospective multicenter studies are needed to further evaluate causal relationships, long-term retention, and implementation feasibility.</p>