Coercive bullying among forensic inpatients: Predictive utility of the VRAG-R for assessing risk of perpetration
Bibliographic record
Abstract
Coercive bullying (i.e., pressuring a peer to do something they do not want to do) is a widespread problem in correctional and secure forensic settings. Though risk factors for other types of bullying (e.g., physical) have been studied, there is limited research investigating risk factors of coercive bullying. The Violence Risk Appraisal Guide-Revised (VRAG-R) is a well-validated and widely used actuarial risk assessment tool for measuring risk of future violence. Given that risk factors for violence overlap with risk factors for bullying, the present study seeks to investigate whether items within the VRAG-R are associated with coercive bullying perpetration in a sample of 94 forensic inpatients in Canada. Data were collected cross-sectionally across four forensic hospitals using a structured interview format. Using a self-report checklist (through interviews) to measure 18 items of coercive bullying behavior over the previous 3 months, 35 participants (37%) disclosed perpetrating coercive bullying against their peers. Coercive bullying perpetration was significantly and moderately associated with two VRAG-R items: younger age at index offense (AUC = .66) and a history of conduct disorder (AUC = .66), while other items also resulted in medium effects. These findings have important implications for understanding risk of coercive bullying perpetration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".