Self-efficacy and Resilience as predictors of Depression among Iranian university students

Background Depression is one of the most prevalent psychological issues among university students, and identifying its key psychological predictors is essential for designing effective prevention and intervention programs. This study aimed to examine the predictive roles of self-efficacy and resilience in explaining depression among students. Method This cross-sectional study was conducted in 2023 among 300 university students selected through convenience sampling. Participants completed the General Self-Efficacy Scale (GSE), the Connor–Davidson Resilience Scale (CD-RISC), and the Beck Depression Inventory (BDI-II). Data were analyzed using Pearson correlation coefficients and stepwise multiple regression to determine the predictive contribution of self-efficacy and resilience subscales to depression. Results The regression analyses indicated that both self-efficacy and resilience significantly predicted depression, jointly explaining a meaningful proportion of variance (R² = 0.33). In the extended model, perception of individual competence, self-efficacy, and control also emerged as significant predictors, increasing the explained variance to 0.35. All regression assumptions were met, and no evidence of multicollinearity was observed across the models. Conclusion Self-efficacy, perceived personal competence, and control emerged as significant predictors of depression, collectively explaining a meaningful portion of variance in students’ depressive symptoms. These findings underscore the importance of psychological strengths in protecting against emotional distress. Strengthening self-efficacy, coping skills, and resilience may therefore serve as effective targets for interventions aimed at reducing depression among university students.