A Schematic Insight to Virtual Reality in Inter-professional Simulation for Enhancing Teamwork among Medical Professionals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Virtual reality (VR)-based interprofessional simulation is a growing educational method of improving teamwork and communication between healthcare professionals. This systematic review and meta-analysis intended to determine the relevance of VR simulation to improve teamwork performances and the communication attributes in different healthcare disciplines. Methods: A comprehensive literature search was conducted across PubMed, Web of Science, and Google scholar in accordance with the PRISMA 2020 guidelines. Interventions that targeted the aspects of interprofessional teamwork and communication attitudes were eligible. Risk of bias was assessed using Cochrane risk of bias tool and Newcastle Ottawa for Randomized control trials (RCTs) and Observational studies, respectively. The certainty of evidence was evaluated with GRADE criteria. Standardized mean differences (SMDs) were determined and were expressed as 95 % confidence intervals (CIs) and the heterogeneity was compared as I2 statistics. Results: A total of 10 studies were included in which VR-based interprofessional simulation was found to produce a substantial positive impact on the performance of teamwork among nursing students (SMD = 0.73; 95% CI: 0.17-1.29; p < 0.0001) or surgeons (M = 7.81; 95% CI: -2.52 to 18.16; p = 0.0007). As a secondary outcome, there was a large positive impact of communication attitudes as well (SMD = 1.02; 95% CI: 0.66 to 1.37; p < 0.0001). These results were stable as confirmed by sensitivity analyses. Discussion: VR-based Inter professional simulations may be utilized in training healthcare workers in building teamwork and communication skills in order to facilitate better collaboration and increased patient safety.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".