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April 5, 2026Stress and Health1 citations

Heterogeneity of Academic Stress Profiles and Depression‐Anxiety Symptom Networks in Left‐Behind Children: A Latent Profile and Simulated Intervention Network Analysis

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KLKuiliang LiLRLei RenRLRui Lang

Key Points

  • To explore academic stress heterogeneity and its relationship with depression-anxiety symptoms in left-behind children.
  • Cross-sectional survey of 10,524 Chinese children comparing left-behind and non-left-behind children
  • Conducted latent profile analysis to identify academic stress subgroups
  • Estimated depression-anxiety symptom networks using Ising and Gaussian graphical models
  • Evaluated simulated interventions using the NodeIdentifyR algorithm
  • Left-behind children exhibited higher academic stress and symptoms of depression and anxiety than non-left-behind children
  • Three academic stress subgroups were identified: moderate (31.44%), high (9.17%), and low (59.39%)
  • Greater severity of depression and anxiety was associated with higher academic stress
  • The high stress subgroup displayed stronger symptom connections; 'Worry' was a core intervention target
  • Simulated interventions showed that addressing 'Decision Hesitation' or 'Worry' significantly reduced symptom risk.

Abstract

Compared with non-left-behind children (NLBC), left-behind children (LBC) face a higher risk of academic stress, depression, and anxiety symptoms due to separation from their parents; however, the heterogeneity of academic stress profiles and their relationships with the symptom network remain insufficiently explored. To address this gap, a cross-sectional survey of 10,524 Chinese children compared LBC (n = 2487) and NLBC. Latent profile analysis (LPA) was first conducted to identify academic stress subgroups among LBC. Subsequently, depression-anxiety symptom networks were estimated using Ising and Gaussian graphical models (GGM), with edge weights derived from regularised logistic regression (Ising) and partial correlation (GGM). Simulated interventions were further evaluated via the NodeIdentifyR algorithm (NIRA). Overall, compared to NLBC, LBC exhibited higher levels of academic stress, depression, and anxiety (ps < 0.001, Cliff's δ = 0.076; Cohen's d = 0.067). LPA revealed three academic stress subgroups: moderate (31.44%), high (9.17%), and low (59.39%). The severity of depression and anxiety symptoms increased with the level of academic stress. The high stress subgroup displayed a sparse network with stronger edges (e.g., A1 'Sudden Fear'-A4 'Physical Symptoms', edge weight = 2.10) compared to moderate- and low-academic stress subgroups. Core nodes with the strongest expected influence were A8 ('Decision Hesitation', moderate subgroup), A2 ('Worry', high subgroup), and D1/D6 ('Sadness' and 'Failure', low subgroup). Simulated interventions indicated that alleviating A8 'Decision Hesitation' or A2 'Worry' most effectively reduced symptom risk (16.66%-30.76%), whereas D8 'Motor' and A7 'Early Departure' were associated with maximal symptom aggravation. Taken together, by integrating LPA-derived academic stress profiles with symptom network analysis, this study reveals distinct symptom associations across subgroups. In the high stress subgroup, symptom A2 ('Worry') is a core intervention target; in the low stress subgroup, A7 ('Early Departure') holds preventive potential. These findings underscore subgroup-specific interventions tailored to individual stress profiles.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd13a79560c99a0a2f03https://doi.org/10.1002/smi.70172
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