Abstract
OBJECTIVE: Self-acceptance is a multifaceted psychological phenomenon that has substantial clinical research implications. The structure of self-acceptance remains poorly understood, despite its significant impact on 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 network was estimated. The model depicted the topic items as nodes, with edges reflecting the regularized partial correlation between them. A node's connectedness to other points in the network is referred to as its centrality. To confirm the trustworthiness of the findings, advanced stability and accuracy analyses were conducted. RESULTS: The study found that item z6 ("I am satisfied with myself") had the highest strength centrality (expected influence EI = 1.478), indicating it is the most central and influential node within the self-acceptance network. Item z4 ("I am always afraid to do things for fear of screwing up", EI = 1.264) and z16 ("I am always worried that people will look down on me", EI = 1.007) also demonstrated high expected influence. The centrality order of network edges and nodes was appropriately predicted. CONCLUSION: The network analysis uncovered intriguing correlations across self-acceptance indicators, necessitating further investigation into the implications of these findings for self-acceptance modeling. The identification of these central items (particularly z6, z4, and z16) provides clear targets for potential psychological interventions aimed at enhancing self-acceptance.