Immersive Virtual Simulation Boosts CPR Competency in Vocational Nursing Students: A Randomized Controlled Trial
Bibliographic record
Abstract
This study investigates the effectiveness of Virtual Simulation Systems (VSS) in improving cardiopulmonary resuscitation (CPR) training outcomes for vocational nursing students through a cluster randomized controlled trial. A total of 100 second-year nursing students from Sichuan Health Rehabilitation Vocational College were divided into an experimental group, which received VSS-based training incorporating virtual reality, real-time feedback, and interactive scenarios, and a control group following traditional manikin-based methods. After a four-week intervention, the experimental group exhibited significantly higher scores in theoretical knowledge (mean 88.68 versus 75.24) and practical skills (mean 85.10 versus 81.52), with statistical significance confirmed. Participants in the VSS group also reported greater satisfaction and engagement, emphasizing the system’s ability to replicate clinical realism and reduce performance anxiety. These results demonstrate that VSS addresses critical gaps in conventional CPR education by enhancing competency through immersive, adaptive learning. The findings support the integration of technology-driven training in vocational nursing programs, particularly in resource-limited settings. Future research should focus on long-term skill retention and scalable implementation strategies to optimize the educational impact of virtual simulations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".