The Use of Immersive Virtual Reality Training for Developing Nontechnical Skills Among Nursing Students: Multimethods Study
Bibliographic record
Abstract
BACKGROUND: Immersive virtual reality (IVR) is a niche technology rising in popularity in nursing education. Although there is an abundance of evidence to demonstrate the effect of virtual reality (VR) on desired learning outcomes, this evidence is limited to technical or procedural skills or managing a single patient with clinical problems. Nontechnical skills (NTS), such as communication, decision-making, teamwork, situation awareness, and managerial skills, have not been explored using IVR technology. OBJECTIVE: This study aimed to (1) investigate the potential efficacy of the IVR system virtual reality hospital (VR-Hospital, or VR-Hosp), a single-user game we developed, on nursing students' NTS, sense of presence in the virtual clinical environment, and satisfaction and self-confidence in learning; (2) identify variables that predict NTS; and (3) explore students' experience in using VR-Hosp. METHODS: A multimethods design with a quantitative and qualitative approach was adopted. Participants were provided with VR-Hosp with 3 scenarios in training. VR-Hosp adopted a multibed, multipatient, multitask approach and was embedded with various clinical situations. Learning outcomes were measured after the training, followed by group interviews. RESULTS: In total, 202 students joined the study. Results revealed high levels of satisfaction and self-confidence in learning. Significant achievement in NTS was perceived by the students. The levels of satisfaction and self-confidence in learning and the involvement and sensory fidelity domains in the sense of presence were positive predictors of NTS. CONCLUSIONS: The promising results offer a basis for designing IVR activities for nursing education. Further investigations are imperative to determine the impact of IVR technology on learning outcomes in clinical practice.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".