Comprehensive review on virtual reality for the treatment of violence: implications for youth with schizophrenia
Bibliographic record
Abstract
Youth violence is a complex and multifactorial issue that has severe health and social consequences. While treatment options exist to treat/reduce violence in at-risk populations such as schizophrenia, there remains limitations in the efficacy of current interventions. Virtual reality (VR) appears to be a unique possibility to expose offenders and to train coping skills in virtual situations that are capable of eliciting aggression-relevant behavior without threatening others. The focus of this paper is to provide a comprehensive review of studies using VR to manage violence across several at-risk populations, with a particular emphasis on youth with schizophrenia. Despite the encouraging success of VR applications for the treatment of different mental health problems, no studies have explored the usability of VR to specifically treat violence in patients with schizophrenia. A limited number of studies have focused on violence risk factors in other mental health problems (i.e., emotion regulation in individual suffering from post-traumatic disorders) that may be targeted in treatments to reduce the risk of violence. The preliminary studies using VR as a therapeutic element have shown reductions in anger, improvements in conflict-resolution skills as well as in empathy levels, and decreases in aggression. Possible applications of these interventions in youth with schizophrenia will be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".