Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study
Bibliographic record
Abstract
PURPOSE: Pediatric trauma education remains expensive and available only to a few providers worldwide. Innovative educational technologies like virtual reality (VR) can be key to decentralizing trauma education. This preliminary validation study evaluates the face and content validity of a VR software designed to enhance pediatric trauma skills. METHODS: Physicians were invited to test a VR software simulating a child with blunt head and truncal trauma. After the simulation, they filled out surveys assessing the face and content validity of the scenario, including the software's realism, interaction, ease of use, and educational content. Additionally, they completed a cybersickness questionnaire. A descriptive statistical analysis was performed. RESULTS: Eleven physicians from eight different countries tested the VR software. Most found it valuable, and realistic and would prefer using it over high-fidelity mannequins for training. The software received more favorable evaluations for non-technical skills training than for technical skills. Regarding cybersickness most participants reported discomfort during the simulation. CONCLUSION: Participants agreed that a VR platform for pediatric trauma is realistic and immersive, and they endorsed it for enhancing performance, particularly in non-technical skills. Most participants, however, faced some discomfort with the technology, and efforts to minimize cybersickness should be made in future implementation, feasibility, and effectiveness studies. LEVEL OF EVIDENCE: IV.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".