The mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury among adolescents: Findings from community and inpatient mental health settings in Ontario, Canada
Bibliographic record
Abstract
Although bullying victimization has been linked to a number of behavioral and emotional problems among adolescents, few studies have investigate the mechanism through which bullying victimization affect non-suicidal self-injury. The objectives of this study were to examine the effect of bullying victimization on non-suicidal self-injury and the mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury among adolescents. Data for this study came from the interRAI Child and Youth Mental Health dataset. A total of 1650 adolescents aged 12-18 years (M =14.56; SD =1.79; 54.2% males) were analyzed. Binary logistic and Poisson regression models were conducted to identify the mediating effect of depressive symptoms on the relationship between bullying victimization and non-suicidal self-injury. Of the 1650 adolescents studied, 611 representing 37% engaged in non-suicidal self-injury and 26.7% were victims of bullying. The effect of bullying victimization on non-suicidal self-injury was partially mediated by depressive symptoms after adjusting for the effect of demographic characteristics, history of childhood abuse, social support, and mental health diagnoses. The contribution of bullying victimization and depression to non-suicidal self-injury adds to the case for the development of trauma-focused interventions in reducing the risk of non-suicidal self-injury among adolescents.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".