Shedding Light on Self-Acceptance: Insights from Network Analysis for Youth Intervention

Objective: Self-acceptance is a multifaceted psychological phenomenon that has substantial clinical research implications. The structure of self-acceptance is not well understood, despite its significance for mental health. Methods: In the current study, self-acceptance was examined in a large sample ( n = 2460) drawn from a highly representative sample of the Chinese general population. In network analysis, a regularized partial correlation networks was estimated. The model depicted the topic items as nodes, with edges reflecting the regularized partial correlation between them. Nodes' connectedness to other points in the network is referred to as their centrality. To confirm the findings' trustworthiness, advanced stability, and accuracy analyses were done. Results: The study found that item 6 ("I am satisfied with myself") had the greatest strong centrality score. The centrality order of network edges and nodes was appropriately predicted. Conclusions: The network analysis uncovered intriguing correlations across self-acceptance indicators, necessitating further investigation into the implications of these findings for self-acceptance modeling.