Abstract
BACKGROUND: Depression poses a considerable personal and public health problem, particularly in the post-epidemic era. The present study aimed to investigate the association between meaning in life (MIL) and perceived social support (PSS) with depressive symptoms among vocational undergraduate students, employing a network analysis approach to gain a deeper understanding of the underlying pathways and to prevent the progression of depressive symptoms into disorders. METHODS: A total of 1367 Chinese vocational undergraduates (M (age) = 20.1, SD = 1.6; 44.7% female) were recruited and were asked to complete a series of questionnaires, including the meaning in life questionnaire, perceived social support scale, and patient health questionnaire. The regularized partial correlation network was estimated. The partial correlations between nodes were calculated as edges. Moreover, network comparison tests were conducted to compare three subnetworks based on different levels of depression (minimal, subthreshold, and moderate/severe). RESULTS: The top strength nodes within each network were identified as sleep and motor in minimal group, anhedonia and concentration in subthreshold group, and anhedonia and sleep in moderate/severe group. Additionally, the bridge strength nodes were determined as MIL-3, MIL-4, sleep, guilt, and school in minimal group; MIL-4, anhedonia, suicide, and friend in subthreshold group; MIL-9, MIL-7, anhedonia, sleep, and family in moderate/severe group. Furthermore, network comparison tests showed significant differences in centrality (all p < 0.05), while network invariance remained constant across groups. Notably, the accuracy and stability coefficients for all network structures were greater than 0.5, indicating stable and reliable results. CONCLUSION: These findings elucidate specific pathways and potential central nodes for interactions of MIL or PSS with depressive symptoms at different levels of depression, providing valuable insights for targeted prevention and intervention strategies.