Interprofessional Team Training With Virtual Reality: Acceptance, Learning Outcome, and Feasibility Evaluation Study
Bibliographic record
Abstract
Background: Effective interprofessional teamwork is vital for ensuring high-quality patient care, especially in emergency medicine. However, interprofessional education often fails to facilitate meaningful interaction among health care disciplines. It is therefore imperative to afford early opportunities for cultivating interprofessional teamwork skills. While in-person simulation-based training has been shown to improve performance, this is resource-intensive, especially if it involves multiple professions. Virtual reality (VR)-based training is an innovative instructional approach that demands fewer resources and offers the flexibility of location-independent learning. Objective: This study aimed to develop and evaluate the acceptance, learning outcome, and feasibility of an interprofessional team (INTEAM) training course that included a VR simulation of a neurological emergency case. Methods: This 1-group study used a pre- and posttest design to evaluate the 2-hour INTEAM training course for nursing and medical students. The course included an e-learning part, VR simulation, and debriefing. The main learning objectives were derived from the entrustable professional activity 6, namely to handle a common problem in emergency medicine (headache due to subarachnoid hemorrhage and epileptic seizure) that requires interprofessional collaboration, including a structured handover. We used validated and self-constructed questionnaires, pre- and posttests, and open questions to assess the acceptance, learning outcome, and feasibility of the course. Results: The data of 42 students (21 nursing and 21 medical students) were analyzed and showed good usability in the System Usability Scale (median 72.5, IQR 65-80). The perception of usefulness (median 6, IQR 5.8-6.9) and ease of use (median 5.9, IQR 5.1-6.3) was good among all students. There was a significant increase in the handover performance from pre- (median 8, IQR 6-9) to posttraining (median 8, IQR 7-9; z=-2.01; P=.045; r=0.33) and of the confidence in caring for patients with seizures (median 3, IQR 2-3 and median 3.5, IQR 3-4, respectively; z=-3.8; P<.001; r=0.60). In 67% (14/21) of the simulations, technical issues occurred, but all simulations could be carried out completely. Conclusions: The new INTEAM training course was well received by nursing and medical students. The handover skills and confidence in caring for patients with seizures were improved after the course. Despite technical challenges with the VR simulations, none required termination, and this demonstrates that our approach is feasible. These promising results encourage the use of VR simulations for team training in the education of nursing and medical students.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".