Multidisciplinary Design–Based Multimodal Virtual Reality Simulation in Nursing Education: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic underscored the necessity for innovative educational methods in nursing. Our study takes a unique approach using a multidisciplinary simulation design, which offers a systematic and comprehensive strategy for developing virtual reality (VR) simulations in nursing education. OBJECTIVE: The aim of this study is to develop VR simulation content for a pediatric nursing module based on a multidisciplinary simulation design and to evaluate its feasibility for nursing education. METHODS: This study used a 1-group, posttest-only design. VR content for pediatric nursing practice was developed by integrating the technological characteristics of a multimodal VR system with the learning elements of traditional nursing simulation, combining various disciplines, including education, engineering, and nursing. A user test was conducted with 12 nursing graduates (preservice nurses) followed by post hoc surveys (assessing presence, VR systems, VR sickness, and simulation satisfaction) and in-depth, one-on-one interviews. RESULTS: User tests showed mean scores of 4.01 (SD 1.43) for presence, 4.91 (SD 0.81) for the VR system, 0.64 (SD 0.35) for VR sickness, and 5.00 (SD 1.00) for simulation satisfaction. In-depth interviews revealed that the main strengths of the immersive VR simulation for pediatric pneumonia nursing were effective visualization and direct experience through hands-on manipulation; the drawback was keyword-based voice interaction. To improve VR simulation quality, participants suggested increasing the number of nursing techniques and refining them in more detail. CONCLUSIONS: This VR simulation content for a pediatric nursing practice using a multidisciplinary educational design model was confirmed to have positive educational potential. Further research is needed to confirm the specific learning effects of immersive nursing content based on multidisciplinary design models.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".