Training and Assessing Teamwork in Interprofessional Virtual Reality–Based Simulation Using the TeamSTEPPS Framework: Protocol for Randomized Pre-Post Intervention Study
Bibliographic record
Abstract
BACKGROUND: Interprofessional teamwork is essential for patient outcomes in emergency medicine; yet, effective training in this area is scarce. Virtual reality (VR) provides a promising, resource-efficient solution for simulating emergency scenarios and facilitating interprofessional collaboration. While VR-based training has shown benefits for medical skill and knowledge acquisition, assessing teamwork within such environments remains a challenge due to the lack of validated measurement tools. Existing teamwork assessment instruments, developed for physical simulations, may not fully apply to VR due to differences in communication modalities, interaction mechanics, and observer perspectives. OBJECTIVE: This study aims to adapt and validate the TeamSTEPPS framework to assess teamwork in VR-based training. Subsequently, these adapted instruments will enable the investigation of whether interprofessional teamwork can be successfully trained in VR scenarios. METHODS: Prior to the study, measurement instruments for subjective (Teamwork Perceptions Questionnaire) and objective teamwork quality (Team Performance Observation Tool, TPOT) will be adapted and validated for use in VR scenarios. Validation of the adapted version of the Team Performance Observation Tool includes expert consensus via a modified Delphi method as well as validity and reliability testing using recorded VR teamwork sessions. The study itself is designed as a prospective pre-post study with a planned enrollment of 65 nursing and 65 medical students working in randomly assigned interprofessional teams. On 3 timepoints (day 1, day 8, and day 15), participants engage in a VR scenario simulating 1 out of 3 different emergency medical conditions (esophageal variceal bleeding, exacerbated chronic obstructive pulmonary disease, and atrial fibrillation due to urinary tract infection). As an intervention, a structured training video on successful teamwork according to the TeamSTEPPS concept is shown on day 8 immediately before the second VR scenario. Teamwork is assessed objectively with the adapted version of the Team Performance Observation Tool and subjectively with the adapted Teamwork Perceptions Questionnaire. Medical performance will be recorded automatically by the VR software based on the medical measures conducted by the team. RESULTS: As of May 2024, a total of 28 interprofessional teams have been enrolled. Data analysis will begin in late 2025. CONCLUSIONS: This study addresses the challenge of adapting teamwork assessment tools to VR environments and may provide insights into the potential of VR-based training for improving interprofessional collaboration in medical education. Future research could include a control group to measure the effects of team training more rigorously or use more enhanced technologies (eg, natural language processing) to capture the full range of teamwork behavior. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68705.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".