ArticleBMC psychology2025
The relationship between academic stress and depressive symptoms in middle school students: a network analysis model.
Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveBased on network analysis methodology, a network model was constructed to elucidate the relationship between academic stress and depressive symptoms among middle school students. By identifying core nodes (high-strength symptoms) and bridge nodes (cross-group associated symptoms) within the two symptom clusters, the study reveals the interactive pathways between these constructs. This approach provides a foundation for precise mental health interventions tailored to middle school students.
methodsA total of 1,576 middle school students in central Anhui Province were selected by the cluster sampling method, and questionnaire surveys were conducted using the Academic Stress Scale for Middle School Students and the Center for Epidemiologic Studies Depression Scale (CES-D). Data analysis was conducted using SPSS 26.0 and R software (Version 4.4.1). The partial correlation network was estimated using the EBICglasso algorithm (γ = 0.5) in R software (Version 4.4.1). Node strength centrality and bridge strength centrality were calculated, and the stability of the network was assessed using the correlation stability (CS) coefficient.
resultsThe network structure of academic stress and depressive symptoms among middle school students comprises 308 valid edges, forming two tightly connected clusters. In the Academic Stress Scale, SS16 (strength = 1.169) and SS12 (strength = 1.136) were identified as core nodes; SS20 (bridge strength = 0.248) and SS14 (bridge strength = 0.185) exhibited higher bridge strengths, with SS20 (bridge strength = 0.248) serving as the critical bridge node.In the Depression Scale, CES-D6 (strength = 1.211) and CES-D3(strength = 1.189) exhibited higher strengths, while CES-D16(bridge strength = 0.189) and CES-D20(bridge strength = 0.157) showed higher bridge strengths. The cross-symptom clusters edge between SS20 (“When I am upset, I feel I have no close friends to talk to.“) and CES-D14(“I felt lonely.“) had the highest edge weight (0.137). The network stability test revealed that both strength centrality (CS = 0.75) and bridge strength centrality (CS = 0.67) exceeded the recommended threshold (> 0.5).
conclusionAcademic stress and depressive symptoms form a dynamic interconnected network through social isolation (SS20) and low life satisfaction (CES-D16), suggesting the need to design targeted intervention strategies focusing on bridge nodes to mitigate their negative impact on middle school students.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.