Implementation of Virtual Reality in Health Professional Higher Education: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The use of virtual reality in higher education show great potential to promote novel and innovative learning experiences. Until recently, virtual reality has mostly been used in technical higher education, but lately medical education programs have begun using virtual reality. Virtual reality for health professional education improves the knowledge and skills of health professionals compared with traditional or other digital education initiatives. However, the implementation of technology in higher education is slow because of barriers to technology use and innovative and successful practices are not shared. It is, therefore, of great interest to explore how virtual reality is implemented in higher health professional and continuing education. OBJECTIVE: The aim of this scoping review is to identify studies that reported implementation of virtual reality in higher health professional education, to identify barriers and facilitators for implementation, and to highlight research gaps in this area. METHODS: The scoping review will be conducted according to JBI Evidence Synthesis methodologies. CINAHL, the Academic Search Elite and Education Source electronic databases, and Google Scholar will be searched for studies published between 2017 and 2022. In addition, manual searching of key items, reference tracking, and citation tracking will be performed. Searches for white papers will also be manually conducted. All authors will independently extract data from full-text papers. We will use qualitative content analysis to abstract the findings. RESULTS: The literature searches were conducted in January and February 2022. The review is expected to be completed by fall 2022, after which time it will be submitted for publication. CONCLUSIONS: We anticipate that, from the review, we will be able to coordinate recommendations for and present the challenges of virtual reality initiatives in health professional education programs. We will present recommendations for future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37222.
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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 | 0.104 | 0.085 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.094 | 0.019 |
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".