Evaluation of a virtual reality enhanced bullying prevention curriculum pilot trial
Bibliographic record
Abstract
INTRODUCTION: Bullying is a widely prevalent public health and safety issue that can have serious long-term consequences for youth. Given the limited efficacy of traditional bullying prevention programs, a need exists for novel, theoretically informed, prevention programming. Construal Level Theory provides a useful framework. METHODS: This study evaluated a pseudo-randomized pilot trial of a virtual reality enhanced bullying prevention program among middle school students (N = 118) in the Midwest United States. Two models were proposed. The first predicts reductions in bullying behavior (traditional bullying, cyberbullying, relational aggression) at post-test, mediated by changes in empathy in the virtual reality condition compared to the control condition. The second predicts increases in school belonging and willingness to intervene as an active bystander at post-test, mediated by changes in empathy in the virtual reality condition compared to the control condition. RESULTS: The virtual reality condition yielded increased empathy from pre-to post-intervention compared to the control condition. Through the mediating role of empathy, changes in the desirable directions were also observed for traditional bullying, sense of school belonging, and willingness to intervene as an active bystander, but not for cyberbullying or relational aggression. CONCLUSIONS: The scope and practical limitations of the virtual reality trial prevented a larger scale and more rigorous evaluation; however, results justify an expanded examination of virtual reality as a youth violence prevention tool.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".