Network analysis of the relationship between self-injury addiction, attachment, and anxiety in adolescents with non-suicidal self-injury
Bibliographic record
Abstract
BACKGROUND: Non-suicidal self-injury (NSSI) has become increasingly prevalent, with its impact growing more severe among adolescents. Addictive NSSI typically manifests through higher frequencies, more severe injuries, and a broader range of affected body areas, potentially leading to trauma, disability, or even suicide. This study aims to explore the complex network relationships among adolescent attachment, anxiety, and NSSI addiction, offering new insights into the mechanisms underlying NSSI addiction in adolescents. METHODS: A total of 1169 adolescent patients with NSSI were enrolled. Demographic questionnaires, the Ottawa Self-Injury Inventory, the Inventory of Parent and Peer Attachment, and the Multidimensional Anxiety Scale for Children were administered for assessment. The complex network relationships among symptoms were analyzed using undirected network analysis and directed Bayesian network analysis, followed by causal inference. RESULTS: The core symptom nodes in the network model included four dimensions of anxiety symptoms: MASC2 (harm avoidance), MASC3 (social anxiety), MASC4 (separation anxiety), and MASC1 (physical symptoms), as well as Peer3 (peer alienation) related to attachment relationships. Undirected network analysis indicated that the key bridging nodes for NSSI addiction were Peer3 (peer alienation) and MASC1 (physical symptoms). Directed acyclic graph (DAG) analysis further confirmed this relationship, demonstrating that these two key bridging nodes directly influence NSSI addiction. Additionally, DAG analysis revealed that MASC3 (social anxiety) indirectly influences NSSI addiction by affecting Peer3 (peer alienation) and MASC1 (physical symptoms). CONCLUSION: Physical anxiety symptoms and peer alienation directly influence NSSI addiction among adolescents. Additionally, social anxiety indirectly influences NSSI addiction by affecting physical anxiety and peer alienation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".