Training police to de-escalate mental health crisis situations: Comparing virtual reality and live-action scenario-based approaches
Bibliographic record
Abstract
Abstract Virtual reality is an emerging frontier that offers immersive simulations with the capacity to revolutionize police training. This study evaluated a scenario-based training approach by comparing the delivery of simulations in Virtual Reality (VR) and Live Action (LA). Participation in this mental health crisis response training program in either format led to a significantly greater acquisition of de-escalation competencies compared to a control group. VR format showed comparable effectiveness to the LA format in bringing about improved de-escalation skills through scenario-based training. The training was equally effective across all officer experience levels. The VR group showed a greater reduction in bias towards mental illness compared to the control group. Further, the VR format was found to be no more cognitively demanding than live action. The article discusses the centrality of de-escalation skills in police practice and considers the larger implications of de-escalation training delivered through virtual reality applications for increased consistency, cost-efficiencies, and professionalization.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".