Evaluation of a virtual reality training tool for firefighters responding to transportation incidents with dangerous goods
Bibliographic record
Abstract
Abstract Access to dangerous goods training for firefighters in remote areas is limited for financial and logistical reasons. Virtual reality (VR) is a promising solution for this challenge as it is cost-effective, safe, and allows to simulate realistic scenarios that would be dangerous or difficult to implement in the real world. However, rigorous evaluations of VR training tools for first responders are still scarce. In this exploratory user study, a simple VR training tool involving two dangerous goods scenarios was developed. In each scenario, trainees learned how to safely approach a jackknifed truck with a trailer and how to collect and communicate information about the transported materials. The tool was tested with a group of 24 professional firefighter trainees ( n = 22) and instructors ( n = 2), who each completed the two training scenarios. The main goal of the study was to assess the usability of the VR tool in the given scenarios. Participants provided feedback on cybersickness, perceived workload, and usability. They also filled out a knowledge test before and after the VR training and gave feedback at the end of the study. The VR tool recorded task completion duration and participants’ navigation and use of tools events. Overall, the tool provided good usability, acceptance, and satisfaction. However, a wide range in individuals’ responses was observed. In addition, no post-training improvement in participants' knowledge was found, likely due to the already high level of knowledge pre-training. Future directions for improving the VR tool, general implications for other VR training tools, and suggestions for future research are discussed.
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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 | 0.001 | 0.000 |
| 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".