Unique and Combined Contribution of Peer Victimization and Maltreatment in Childhood to Young Adults’ Anxiety, Depression, and Suicidality: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract BackgroundChild maltreatment and peer victimization are known to be major risk factors for depression and suicidal behavior. Furthermore, child maltreatment increases the risk for victimization by peers. Our objective was to distinguish the contribution of maltreatment by parents and peer victimization to mental health problems in young adulthood. Specifically, we tested whether peer victimization alone or in combination with parental maltreatment before 18 years was associated with anxiety, depression, and suicidal thoughts and behaviors at age 21 years.MethodsWe used data collected in the i-Share (internet-based students’ health research enterprise) study in France from February 2013 to September 2019 (N = 2271 participants). We conducted multinomial and binary logistic regression analyses to assess the unique and cumulative contribution of peer victimization and parental maltreatment with anxiety, depression, and suicidality. ResultsAlmost one third of students (28.8%) reported at least one mental health problem; 29.8% reported peer victimization only; 7.5% reported parental maltreatment only; and 10.3% reported both maltreatment and victimization. In multivariate models, compared to participants who did not experience maltreatment or peer victimization, those who experienced peer victimization only were more likely to present anxiety (aOR: 1.90; 95% CI: 1.50-2.40), depression (aOR: 1.95; 95% CI: 1.46-2.60), or suicidal ideation without (aOR: 1.62; 95% CI: 1.26-2.09) and with attempt (aOR: 2.70; 95% CI: 1.51-4.85). Similar associations were observed for those who were maltreated only. Those experiencing both maltreatment and peer victimization were at increased risk for depression (aOR: 2.63; 95% CI: 1.79-3.86) and for suicidal ideation with (aOR: 9.19; 95% CI: 4.98-16.92) and without attempt (aOR: 2.64; 95% CI: 1.86-3.76).ConclusionsIndividual and combined exposure to parental maltreatment and peer victimization in childhood or adolescence was associated with increased risk for anxiety, depression and suicidal behaviors. Peer victimization seems to have a specific role on mental health disorders not otherwise explained by polyvictimization. Since peer victimization is a frequent and evitable child abuse type, the findings have implications for policies to prevent and deal with.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".