摘要
目的:使用两种网络分析方法,探索大学生进食障碍亚临床状态(sub-clinical eating disorders,简称SCED)下,进食障碍症状间的关系.方法:采用进食障碍检查自评问卷,筛选出247名处于进食障碍亚临床状态的大学生进行分析,分别基于高斯图形模型和贝叶斯爬山算法构建了进食障碍亚临床状态的正则化偏相关网络和有向网络.结果:在正则化偏相关网络中,思考进食而无法集中注意力是进食障碍亚临床状态症状网络中影响力最高的症状,其次是对体重不满;在有向网络中,思考进食而无法集中注意力是网络中优先级最高的节点.结论:临床工作者在诊疗筛查过程中,需关注与进食相关的认知症状,以尽早识别进食障碍亚临床状态个体.
Abstract
Objective:To investigate the network of eating disorder symptoms in college students with sub-clinical eat-ing disorders based on network approach.Methods:A total of 247 college students with sub-clinical eating disorders were included in analysis after reporting Eating Disorder Examination Questionnaire.Gaussian graphical models and Bayesian hill climbing algorithms were used to construct symptom networks of sub-clinical eating disorders.Results:LASSO net-work showed that difficulty concentrating due to thoughts of eating was the most central symptom,followed by dissatisfac-tion with weight.Bayesian network showed that difficulty concentrating due to thoughts of eating was the highest priority node.Conclusion:Psychiatrists should pay attention to the cognitive symptoms associated with eating in the early screen-ing for individuals with SCED.
维普
万方数据