ArticleDiscover mental health2025
Network analysis of self-acceptance structure in adolescents.
Article in Discover mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
objectiveSelf-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.
methodsIn 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.
resultsThe 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.
conclusionThe 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.
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