Virtual Violence as a Contributing Factor to the Mental Health of Young People: A Scoping Review
Bibliographic record
Abstract
Virtual violence is facilitated by anonymity and the presence of aggressors on the internet, and the characteristics of this phenomenon amplify both the reach and intensity of the suffering experienced by victims-particularly in the psychological realm of young people. This review aimed to identify the mental health consequences of virtual violence among young students who use digital technologies, seeking to uncover patterns, experiences, and impacts for those involved. The databases consulted were EMBASE, Scopus, MEDLINE, CINAHL, LILACS, and Web of Science. The sources found were subjected to a two-stage screening process conducted by three reviewers. In the end, 18 articles were included. The studies were organized into five dimensions, and the findings indicated that involvement in cyberbullying-whether as a victim or a perpetrator-predicts greater psychological suffering for both parties. Gender and sexual orientation were found to be contributing factors to the worsening of participants' mental health. By contrast, parental and school involvement in young people's lives had a positive effect, both in reducing the number of victims and in helping to contain aggressors. This review reinforces the importance of recognizing and classifying cyberbullying as a form of violence. It also encourages the development of targeted prevention programs that raise awareness among all involved-especially families and school staff-who play a crucial role in supporting victims and breaking these patterns of violence.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".