درخت حوزه‌های تخصصی

Aim: The growing prevalence of digital games and their potential impact on mental health highlight the need to identify protective mechanisms against possible negative effects. The present study aimed to investigate the role of parental phubbing and ego-resilience in adolescents’ tendency toward digital game addiction: a neural network approach. Method: This research employed a descriptive-correlational design. The statistical population included all male high school students (second cycle) in public schools of Karaj during the 2024 academic year. A total of 300 students were selected using a cluster random sampling method and completed the Digital Game Addiction Scale by Başol et al. (2018), the Parental Phubbing Scale by Ding et al. (2020), and the Ego-Resilience Scale by Block and Kremen (1996). The collected data were analyzed through an artificial neural network using the multilayer perceptron (MLP) approach in SPSS version 26. Results: The findings indicated that parental phubbing and ego-resilience significantly contributed to predicting digital game addiction among adolescents (p< 0.01). Neural network analysis revealed that parental phubbing positively predicted digital game addiction, whereas ego-resilience negatively predicted it. Parental phubbing emerged as the strongest predictor (100%), while ego-resilience served as a protective factor (57.8%). Moreover, the artificial neural network model accurately captured the trends and variations of digital game addiction based on these variables. Conclusion: The results demonstrated that parental phubbing and ego-resilience are critical factors in explaining adolescents’ digital game addiction. Moreover, the artificial neural network model showed high accuracy in predicting the intensity and course of this addiction.

The present study aimed to predict guilt based on social anxiety and social-emotional loneliness in working women seeking divorce. This study is applied in terms of its purpose and descriptive correlation type. The statistical population of this study consisted of all working women seeking divorce in Tehran in 2024. 200 people were selected through purposive sampling from the family counseling centers of the judiciary. The research tools included the Guilt Inventory by Kugler and Jones (1992); the Social Phobia Inventory by Connor et al. (2000) and the UCLA Loneliness Scale by Russell (1996). The research data were analyzed using stepwise regression analysis and SPSS-22 software. The results showed that there is a positive and significant relationship between social anxiety and loneliness with guilt at the 0.01 level. Social anxiety (P=0.009, β=0.183) positively and at a significance level of 0.01 predicted guilt, and loneliness (P=0.005, β=0.197) positively and at a significance level of 0.01 predicted guilt. In general, the results showed that guilt feelings are predicted based on social anxiety and social-emotional loneliness in working women seeking divorce.