Virtual reality simulation enabling high level immersion in undergraduate nursing education: A systematic review
Bibliographic record
Abstract
Background and objectives: Virtual reality simulation (VRS) can be used to complement experiential learning, as it enables nursing students to further learn and refine nursing skills outside of the clinical setting. However, gathering evidence for its effectiveness as a teaching method in achieving learning outcomes is still ongoing, and thus there is a lack of systematic synthesis. The objective of this systematic literature review is to analyze VRS scenarios with a high level of immersion and their impact on learning outcomes in nursing education.Methods: A literature search was performed in the MEDLINE, CINAHL, and ERIC databases in November 2022. As a result, fifteen studies were included and analyzed using deductive content analysis.Results: The studies reported twelve different scenarios for virtual reality simulations with high levels of immersion, the focus of which was on acute critical care, broader nursing processes, neonatal and pediatric care, single nursing interventions, and observation of patients’ symptoms. The associated learning objectives were mainly achieved in the domains of cognition and psychomotor skills.Conclusions: There are several VRS scenarios that show potential for use in nursing education. The VRS scenarios are effective in improving learning outcomes, particularly those related to knowledge and skills. Overall, the supportive body of evidence gained through this review may help nurse educators in integrating virtual simulations in their curricula. In the future, nursing and adult learning theories should be given greater consideration, and the aspect of affective learning could be included in design and implementation. Moreover, future research could benefit from exploring the long-term effects of learning after using VRS with a high level of immersion to provide valuable evidence for developing VRS teaching methods in nursing.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".