The mediating roles of stressful life events and negative affect in the relationship between childhood maltreatment and non-suicidal self-injury among clinical adolescents
Bibliographic record
Abstract
Background: Non-suicidal self-injury (NSSI) is a significant public health concern among adolescents, particularly in psychiatric settings, where prevalence rates exceed those observed in the general community. Childhood maltreatment (CM) is a known risk factor for NSSI; however, the mechanisms linking CM to NSSI are not fully understood.Objective: This study explored the mediating roles of stressful life events (SLEs) and negative affect (depression and anxiety) in the relationship between CM and NSSI, grounded in the cumulative adversity theory.Methods: In this cross-sectional survey, 226 Chinese adolescents (Mage = 14.76, SD = 1.70) admitted to a psychiatric unit participated. Measures included the Childhood Trauma Questionnaire (CTQ-SF), the Adolescent Self-Rating Life Events Checklist (ASLEC), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Ottawa Self-Injury Inventory Chinese Revised Edition (OSIC). Structural equation modelling (SEM) was used to analyze mediation pathways.Results: Stressful life events and negative affect fully mediated the relationship between childhood maltreatment and NSSI. Specifically, CM indirectly influenced NSSI severity through increased negative affect (β = 0.088, 95% CI: 0.014–0.186, p = .039) and through a sequential pathway involving both SLEs and negative affect (β = 0.137, 95% CI: 0.072–0.251, p = .002). However, the pathway from CM to NSSI via SLEs alone was not significant (β = −0.053, 95% CI: −0.267 to 0.093, p = .565).Conclusion: The findings align with cumulative adversity theory, suggesting that childhood maltreatment elevates NSSI risk by increasing emotional distress in response to subsequent stressful life events. Targeted interventions should focus on helping at-risk adolescents manage stress and strengthen emotional resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".