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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".