Empathy in Police Officers Undergoing De-escalation Simulation Training: A Comparison Between Virtual Reality and Live Action Modalities
Bibliographic record
Abstract
This study compared empathy among police officers undergoing mental health crisis de-escalation training in virtual reality and live action training modalities. The study included police officers across different police services in Ontario (N=63) and evaluated the efficacy of the Mental Health Crisis Response Training (MHCRT, Lavoie et al., 2020) program delivered across virtual reality and live action modalities against a control group that did not receive the training program. After collection of participants’ demographics, empathy scores, and de-escalation competency scores, a series of correlations, ANOVAs and ANCOVAs were conducted. Results showed that participants receiving MHCRT virtual reality and live action formats demonstrated no significantly different effects on empathy based on modality, with both formats displaying an increase in empathy over time. General empathy was found to be related to having multiple de-escalation strategies in the participants’ repertoire, while state empathy towards the specific character in crisis was not significantly related to specific de-escalation strategies. This relationship between general empathy and de-escalation competencies provides evidence for the importance of empathy among police officers, specifically with regard to hiring practices and specialization in mental health crisis response teams. The evaluation of the MHCRT program in both live action and virtual reality training modalities contributes to the limited research on the outcome of scenario-based police training programs and its efficacy in enhancing empathy and de-escalation competencies among police officers. This scenario-based MHCRT program is the first of its kind and opens the door to the possibility of cross-province scalability and standardization for police de-escalation training, with an emphasis on a relational policing approach to serving community members in mental health crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".