A Novel Scenario-Based, Mixed-Reality Platform for Training Nontechnical Skills of Battlefield First Aid: Prospective Interventional Study
Bibliographic record
Abstract
BACKGROUND: Although battlefield first aid (BFA) training shares many common features with civilian training, such as the need to address technical skills and nontechnical skills (NTSs), it is more highly scenario-dependent. Studies into extended reality show clear benefits in medical training; however, the training effects of extended reality on NTSs, including teamwork and decision-making in BFA, have not been fully proven. OBJECTIVE: The current study aimed to create and test a scenario-based, mixed-reality platform suitable for training NTSs in BFA. METHODS: First, using next-generation modeling technology and an animation synchronization system, a 10-person offensive battle drill was established. Decision-making training software addressing basic principles of tactical combat casualty care was constructed and integrated into the scenarios with Unreal Engine 4 (Epic Games). Large-space teamwork and virtual interaction systems that made sense in the proposed platform were developed. Unreal Engine 4 and software engineering technology were used to combine modules to establish a mixed-reality BFA training platform. A total of 20 Grade 4 medical students were recruited to accept BFA training with the platform. Pretraining and posttraining tests were carried out in 2 forms to evaluate the training effectiveness: one was knowledge acquisition regarding the NTS and the other was a real-world, scenario-based test. In addition, the students were asked to rate their agreement with a series of survey items on a 5-point Likert scale. RESULTS: A battlefield geographic environment, tactical scenarios, scenario-based decision software, large-space teamwork, and virtual interaction system modules were successfully developed and combined to establish the mixed-reality training platform for BFA. The posttraining score of the students' knowledge acquisition was significantly higher than that of pretraining (t=-12.114; P≤.001). Furthermore, the NTS score and the total score that the students obtained in the real-world test were significantly higher than those before training (t=-17.756 and t=-21.354, respectively; P≤.001). However, there was no significant difference between the scores of technical skills that the students obtained before and after training. A posttraining survey revealed that the students found the platform helpful in improving NTSs for BFA, and they were confident in applying BFA skills after training. However, most trainees thought that the platform was not helpful for improving the technical skills of BFA, and 45% (9/20) of the trainees were not satisfied with the simulation effect. CONCLUSIONS: A scenario-based, mixed-reality platform was constructed in this study. In this platform, interaction of the movement of multiple players in a large space and the interaction of decision-making by the trainees between the real world and the virtual world were accomplished. The platform could improve the NTSs of BFA. Future works, including improvement of the simulation effects and development of a training platform that could effectively improve both the technical skills and NTSs of BFA, will be carried out.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".