青少年网络游戏成瘾的预测：机器学习视角下的个体、家庭和学校因素洞察

Game addiction is increasingly acknowledged as a major adolescent mental health concern worldwide. To better understand its development, it is essential to identify both risk and protective factors. However, most existing studies have focused on only a limited number of variables. To address this gap, this study adopted machine learning to identify the most important predictors across individual, family, and school domains. The analysis was conducted with a sample of 21,239 Chinese parent-adolescent pairs (for parents: 68.92% mothers; M age  = 40.42 years, SD  = 5.78&#