A network analysis of social problem-solving and anxiety/depression in adolescents

Bibliographic Data

ID15520232
AuthorsQian-Nan Ruan (Wenzhou City People's Hospital), Ce Chen (0009-0000-9324-5709, Wenzhou City People's Hospital), Deguo Jiang (Wenzhou City People's Hospital), De-Guo Jiang, Wen-Jing Yan, Wen‐jing Yan (0000-0001-6413-2414, Wenzhou University, corresponding author), Lin Zhang (0000-0001-5966-3827, Wenzhou City People's Hospital, corresponding author), Zhang Lin (0000-0002-8421-879X)
Year2022
Volume13
Pages921781-921781
Publication date2022-08-10
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2022.921781
PMID36032238
OpenAlexW4290989919
LanguageEN
Citations received3
References cited58

Social problem-solving (SPS) involves the cognitive-behavioral processes through which an individual identifies and copes with everyday problems; it is considered to contribute to anxiety and depression. The Social Problem-Solving Inventory Revised is a popular tool measuring SPS problem orientations and problem-solving styles. Only a negative problem orientation (NPO) is considered strongly related to anxiety and depression. In the present study, we investigated the detailed connections among the five components of SPS and 14 anxiety-depression symptoms and specified the role of NPO and other components in the anxiety-depression network. We employed network analysis, constructed circular and multi-dimensional scaling (MDS) networks, and calculated the network centrality, bridge centrality, and stability of centrality indices. The results were as follows: (1) the MDS network showed a clustering of anxiety and depression symptoms, with NPO and avoidance style components from SPS being close to the anxiety-depression network (demonstrated by large bridge betweenness and bridge closeness); (2) the NPO and positive problem orientation from SPS were most influential on the whole network, though with an opposite effect; (3) strength was the most stable index [correlation stability (CS) coefficient = 0.516] among the centrality indices with case-dropping bootstraps. We also discussed this network from various perspectives and commented on the clinical implications and limitations of this study

Anxiety · Betweenness centrality · Centrality · Closeness · Cluster analysis · Clustering coefficient · Cognition · Depression (economics · Psychiatry · Child and Adolescent Psychosocial and Emotional Development · Clinical Psychology · Computer Science · Mental Health Research Topics · Psychology · Social Representations and Identity · Artificial Intelligence