Virtual reality training programs in disaster preparedness: a systematic review
Bibliographic record
Abstract
The demand for effective disaster preparedness training in hospitals is steadily increasing, as healthcare staff need to handle emergencies efficiently while providing patient care. However, conventional training methods like live drills and tabletop exercises can require significant resources, limit participation, and disrupt hospital operations. In light of these challenges, Virtual Reality (VR) training has emerged as a modern solution, offering an innovative way to improve training efficiency without interfering with routine hospital activities. This study, therefore, examines how VR training compares to traditional methods in preparing hospital personnel for disasters, focusing specifically on effectiveness and cost. We reviewed studies concerning the effectiveness and economic assessments of VR compared to traditional methods. To answer the research questions, we followed the Canada Drug Agency’s guides on conducting a health technology assessment. Articles were identified from both peer-reviewed and gray literature. The review highlights eight pertinent studies demonstrating VR’s advantages, including boosting knowledge retention, performance, and overall confidence among healthcare professionals in disaster preparedness. Furthermore, the economic analysis indicates that although the initial costs of VR synchronous training may be higher, the long-term savings from reduced ongoing training and maintenance can lead to greater cost efficiency. In addition, while VR training presents safety benefits, challenges such as cybersickness and accessibility concerns have also been observed. VR training offers considerable advantages over traditional disaster preparedness methods and significantly improves healthcare workers’ confidence and skills. Despite the high upfront costs, the potential long-term financial benefits appear promising. To maximize results, funding for VR initiatives must emphasize demonstrated effectiveness alongside ongoing research to explore broader applications and address the challenges posed by these emerging technologies.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".