This study examined the relationship between retrospective childhood emotional abuse (CEA) and adult depressive symptoms by investigating the mediating role of cognitive rumination and the moderating role of psychological resilience within a Transylvanian-Hungarian sample. A cross-sectional sample of Hungarian-speaking adults (N = 223, M_age = 24.9 years) completed an online battery of validated self-report measures assessing childhood emotional abuse, depressive symptoms, rumination, and resilience. Mediation and moderation analyses were conducted using a bootstrapping procedure with 5,000 resamples.
The findings supported a partial mediation model, indicating that rumination represented a substantial indirect pathway linking childhood emotional abuse to depressive symptoms. Specifically, the indirect effect accounted for approximately 74.7% of the total effect. Furthermore, resilience significantly moderated the relationship between childhood emotional abuse and depressive symptoms (b = -0.04, p < .001), suggesting that the association between early emotional abuse and adult depressive symptomatology was weaker among individuals reporting higher levels of psychological resilience.
These findings are consistent with cognitive vulnerability models of depression and suggest that repetitive negative thinking and resilience processes may play important roles in understanding individual differences in psychological adjustment following childhood emotional abuse. Although the cross-sectional design precludes causal inferences, the results provide additional evidence from an understudied cultural context and may have implications for interventions aimed at reducing maladaptive rumination and strengthening resilience among individuals exposed to early emotional adversity.
K. Király, Bálint Dimén, Iacobina Carmen Zau et al.· Brain: Broad Research in Art...· 0 citations
The growing integration of generative artificial intelligence (AI) in education requires educators to develop not only operational competencies but also confidence in using AI tools effectively. The present study examined the predictive relationship between the multidimensional construct of ChatGPT literacy and AI self-efficacy among educators working in Romanian educational settings. A sample of 393 educators from Western Romania completed the ChatGPT Literacy Scale (and the AI Self-Efficacy subscale of the Meta AI Literacy Scale. Reliability analyses indicated good to excellent internal consistency across all literacy dimensions (α = .80–.95) and AI self-efficacy (α = .89). To model nonlinear and hierarchical relationships among predictors, a Decision Tree Regression approach was implemented in JASP. The five ChatGPT literacy dimensions, technical proficiency, critical evaluation, communication proficiency, ethical competence, and creative application, were entered as predictors of AI self-efficacy. The model explained 53.2% of the variance in AI self-efficacy (R² = .532), demonstrating moderate predictive performance (MSE = 0.517; RMSE = 0.719). Feature importance analysis revealed that technical proficiency was the strongest predictor (40.07%), followed by ethical competence (18.12%), critical evaluation (14.99%), communication proficiency (14.01%), and creative application (12.80%). The first and most informative split occurred on technical proficiency, highlighting its importance in shaping educators’ perceived AI capability. These findings indicate that technical proficiency emerged as the strongest predictor of AI self-efficacy among educators. The results have implications for AI-focused professional development programmes, emphasising the importance of structured technical training alongside ethical and critical competencies.
Ioana-Eva Cădariu, Loredana-Ileana Vîșcu, Cristian Delcea et al.· Brain: Broad Research in Art...· 0 citations
