Examining the effectiveness of a police developed cardio-pulmonary resuscitation virtual reality training program
Bibliographic record
Abstract
Abstract In 2020 the Saif Bin Zayed Academy for Security & Policing Sciences in the United Arab Emirates created a Police Virtual Training Centre, known as the Abu Dhabi Police Virtual Training Centre. Since their inception they have internally developed more than 12 virtual reality simulations. This includes a Cardio-Pulmonary Resuscitation (CPR) Virtual Reality (VR) Training Program. Delivered to both police officers and civilian employees the CPR VR replicates a realistic cardiac arrest incident. The purpose of this study is to evaluate the effectiveness of this VR simulation. This is achieved by using a quantitative survey methodology to test the knowledge acquired from two groups who received CPR training using VR and a second who received it using traditional classroom tuition. Results are compared inferential statistics and identified no significant difference in test outcomes, indicating the VR simulation is safe. In addition, we surveyed both groups regarding their views and perspectives of the training environment and we used the presence scale for virtual reality to test the degree of immersion, environmental fidelity and for negative impacts. Correlational analysis identified a strong link between the immersive nature of VR, and levels of engagement and realism. Negative effects of discomfort and disorientation were strongly correlated with one another but were not linked to the level of immersion. The findings are discussed in the context of potentially using VR to supplement or replace existing police CPR training, and the wider considerations regarding developing VR within policing.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".