Abstract
Background: Child maltreatment (CM) often results in the co-occurrence of depressive and aggressive-defiant symptoms. However, it remains unclear whether the symptom relationships and putative directionality of these symptoms vary according to CM subtype. Objective: This study examined the symptom network and potential directionality of depressive and aggressive-defiant symptoms in physically abused, emotionally abused, and neglected children in South Korea. Participants and setting: From a nationwide repository, we analyzed cross-sectional data of 3384 children officially identified as victims of CM by a child protection agency. Methods: For each CM subtype, Gaussian graphical models identified central and bridge symptoms, whereas directed acyclic graphs (DAGs) analyses inferred directionality among depressive and aggressive-defiant symptoms. Results: Anger-related symptoms were the most common across CM subtypes. Negative self-concept symptoms were the most central symptoms in the physical and emotional abuse networks, whereas depressive mood and negative interpersonal feedback were the key symptoms in the neglect network. Irritability consistently served as a key bridge symptom linking the two symptom clusters. Negative interpersonal feedback was also a significant bridge between emotional abuse and neglect network symptoms. DAG analyses revealed the subtype-specific putative directionality of comorbidities. Conclusion: CM subtype distinctly shaped the interrelations and directionality of depressive and aggressive-defiant symptoms. Interventions targeting identified central and bridging symptoms may improve tailored treatment approaches.
| Original language | English |
|---|---|
| Article number | 107889 |
| Journal | Child Abuse and Neglect |
| Volume | 173 |
| DOIs | |
| State | Published - Mar 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Aggressive-defiant problems
- Child maltreatment
- Comorbidity
- Depression
- Network analysis
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