Exploring the use of immersive virtual reality in nursing education: A scoping review
Bibliographic record
Abstract
Background Virtual reality is an emerging technology for nursing education. This technology can provide visual, motor, and auditory immersion into a virtual environment, thereby mimicking reality. Virtual reality may be an effective pedagogical tool for nursing educators to meet the learning needs of nursing students. Objective The aim of this scoping review was to map the existing literature on the use of immersive virtual reality implemented within nursing education programs with nursing students. Design Scoping review following established methodology. Data sources A database search of ERIC (OVID), Medline (OVID), PubMed, Web of Science, and CINAHL Plus with Full Text, and Nursing and Allied Health Database. Records published between January 2010 and August 2022. Review methods Two reviewers independently screened titles and abstracts of 2115 articles. Fourteen articles were included in this scoping review. Results Five major themes were identified in the thematic analysis: Self-confidence, Skill acquisition, Improved learning outcomes, Perspective taking, and Promoting engagement. Immersive virtual reality is being utilized in a variety of clinical settings and with different intended purposes. Most virtual reality research is focused on end-user satisfaction and perceptions of usability. Conclusions This scoping review provides a comprehensive understanding of the use of immersive virtual reality within nursing education. There is a range of definitions of virtual reality utilized within nursing literature. Further research is necessary to study this growing area of technology for nursing education.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".