Psychological Resilience and Health-Oriented and Internalizing Problems: The Chain Mediating Effect of Family Functioning and Parent-Child Conflict Using Artificial Neural Network (ANN) Modeling

نویسندگان

1 Teachers’ College, Beijing Union University, Beijing, China, 100011

2 Teachers’ College, Beijing Union University, Beijing, China, 100011

doi
10.22034/ircmj.2025.506829.1890
چکیده

Background and Objectives: Psychological resilience plays a crucial role in child development, impacting emotional and behavioral outcomes. Understanding the dynamics of family functioning and parent-child conflict is essential for addressing internalizing problems in children. This study examines the influence of maternal psychological flexibility on internalizing problems in preschool children, while also investigating how family functioning and parent-child conflict mediate this relationship.    Methods: A total of 889 mothers of preschool children participated, completing various assessments, including the Parental Psychological Flexibility Questionnaire, the Strengths and Difficulties Questionnaire (Parent version), the McMaster Family Assessment Device, and the Child-Parent Relationship Scale.    Results: The results indicated that maternal psychological flexibility by itself had no significant effect on children's internalizing problems. However, it was found that family dynamics and conflicts between parents and children served as mediators in this relationship. In essence, these two elements combined to shape how maternal flexibility influenced children's emotional difficulties. Enhancing maternal psychological flexibility can contribute to improved family functioning, reduced parent-child conflict, and ultimately aid in preventing and alleviating children's internalizing problems.    Conclusion: This study used an artificial neural network (ANN) to investigate the relationships between maternal psychological flexibility, family functioning, parent-child conflict and children's internalizing problems. The neural network predictions were verified using cross-validation techniques.