感觉
心理学
惊恐障碍
焦虑
恐慌
临床心理学
萧条(经济学)
苦恼
躯体化
横断面研究
精神科
心理治疗师
医学
社会心理学
病理
经济
宏观经济学
作者
Jerzy A Sobański,Katarzyna Klasa,Edyta Dembińska,Michał Mielimąka,Anna Citkowska-Kisielewska,Patrycja Jęda,Krzysztof Rutkowski
标识
DOI:10.1016/j.jad.2023.06.040
摘要
Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare. Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980–2015. Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients. Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students. Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.
科研通智能强力驱动
Strongly Powered by AbleSci AI