Virtual Reality Technology in Nursing Professional Skills Training: Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Nursing professional skills training has undergone significant transformation due to the exponential growth of computer and medical technology. The innovative use of virtual reality (VR) in nursing education has emerged as a cutting-edge technical support technique that has gained attention as a highly effective method for improving nurse training quality. OBJECTIVE: This study aims to review the current status of VR technology in nursing professional skills training, research hotspots, and emerging trends in the last 15 years. METHODS: The Web of Science Core Collection database was used to search for literature on VR technology in nursing professional skills training covering the period from 2006 to 2022. Biblioshiny (K-Synth Srl) was used to import and convert the records to Bibliometrix (K-Synth Srl) for analysis, and R (R Core Team) was used for descriptive bibliometric analysis. VOSviewer (Leiden University) was used to cluster co-occurring keywords, and Scimago Graphica (version 1.0.16; Scimago Lab) was used to generate a geographical visualization of published countries and regions. RESULTS: A total of 1073 papers were analyzed, indicating a surge in research on the application of VR in nursing professional skills training in recent years, as evidenced by a positive trend in annual publication of relevant literature. The majority of studies were from the United States (n=340) and Canada (n=107), and Margaret Verkuyl was the most prolific author, leading the way with 9 publications. Furthermore, "Computerized Virtual Patients in Health Professions Education: a Systematic Review and Meta-Analysis" was the most frequently cited reference. Keywords such as education, simulation, skills, students, and care were most commonly used by researchers. CONCLUSIONS: The bibliometric analysis provides a comprehensive overview of the use of VR in nursing professional skills training, indicating that VR-based training is an effective means of improving the skills and competencies of nursing students and professionals alike. The COVID-19 pandemic has reinforced the importance of developing VR-based distance education, despite challenges such as integrating virtual and real-world training and mitigating safety risks.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.067 | 0.329 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".