Peer victimization through a trauma lens: Identifying who is at risk for negative outcomes
Bibliographic record
Abstract
Peer victimization is a chronic stressor that occurs within the context of peer interactions and has been robustly associated with numerous negative psychological and social adjustment problems. Although increased frequency of peer victimization has been linked to psychosocial problems, few researchers have studied the role of duration and pervasiveness of victimization (i.e., number of places it occurs). The objective of this study was to examine how frequency, duration, and pervasiveness of peer victimization are associated with youth adjustment. Canadian adolescents (N = 879), ages 12-18 completed an online survey about experiences with peer victimization. Youth also answered questions about internalizing problems, distress, relationship quality with family, friends, and adults in their school and community, as well as academic functioning. Data were analyzed using multinomial logistic regression modeling. Both duration and pervasiveness of peer victimization were predictive of increased internalizing problems, distress, relationship problems, and academic difficulties. Duration and pervasiveness of peer victimization were identified as important factors to consider when predicting youth psychosocial adjustment. By asking questions about these situational factors, parents, teachers, and healthcare providers may more effectively identify youth who are at risk for experiencing mental health problems associated with peer victimization.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".