Mental health problems among university students predated COVID-19, but pandemic-period conditions coincided with changes in how academic, relational, behavioral, and internalizing difficulties co-occurred. Prior studies often modeled single outcomes or symptom clusters separately. This secondary analysis used a construct-level network to describe conditional associations among contextual stressors, adaptive-functioning indicators, and multidimensional mental health indicators in one archived student dataset. We analyzed cross-sectional survey data from 10,745 students collected January 13-March 26, 2021. Fourteen nodes were included: media time, online adaptation, learning efficiency, family conflict, friendship change, epidemic attitude, epidemic attention, exercise, perceived stress, anxiety, depression, sleep problems, loneliness, and resilience. After correcting exercise-frequency coding and anxiety-score derivation errors, we estimated an EBICglasso-regularized partial-correlation network using Spearman correlations. Expected influence, bridge expected influence, and nodewise R2 were interpreted as descriptive, model-dependent indices. We examined edge accuracy, centrality stability, bridge stability, edge-weight differences, data-quality exclusions, alternative estimators, community definitions, and counterintuitive partial edges. The fitted network retained 53 of 91 possible edges (density = 0.582). In this specification, depression, perceived stress, and anxiety had the highest expected-influence values (1.428, 1.064, and 0.782). The strongest absolute partial-correlation edges linked sleep problems with depression (0.736), depression with anxiety (0.668), anxiety with perceived stress (0.459), and sleep problems with anxiety (-0.423). Under the prespecified two-community partition, bridge expected influence was largest for perceived stress (0.461), loneliness (0.343), family conflict (0.233), epidemic attitude (0.184), and learning efficiency (0.126). Expected influence and bridge expected influence showed high case-dropping stability within this analytic specification (CS = 0.75). Data-quality exclusions and an ordinal/mixed-correlation EBICglasso model yielded estimates very similar to the primary network, whereas an unsigned-weight mixed graphical model yielded different centrality rankings. The findings describe a construct-level snapshot of conditional associations among students from one institutional context during a specific COVID-19 period. Perceived stress showed the highest and most stable bridge expected-influence value across the tested community specifications, while other bridge rankings and centrality values were specification-dependent. The results are hypothesis-generating, not evidence of causal pathways, intervention targets, or population-representative student mental health structure.
Cui et al. (Fri,) studied this question.