Virtual Reality for Developing Patient-Facing Communication Skills in Medical and Graduate Education: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Clinician-patient communication is an integral component in providing quality medical care. However, research on clinician-patient communication has shown overall patient discontent with provider communication skills. While virtual reality (VR) is readily used for procedural-based learning in medical education, its potential for teaching patient-facing communication skills remains unexplored. This scoping review aims to evaluate the effectiveness and feasibility of VR applications used for patient-facing communication skills development in medical education. OBJECTIVE The primary objective is to synthesize and evaluate the effectiveness of available VR tools and applications used for patient-facing communication skills development in medical education. The secondary objectives are to (1) assess the feasibility of adapting VR applications to develop patient-facing communication skills in medical education and (2) provide an overview of the challenges associated with adapting VR applications to develop patient-facing communication skills in medical education. METHODS A total of 4 electronic databases (ERIC, Embase, PubMed, and MEDLINE) were searched for primary peer-reviewed articles published through April 11, 2023. Articles evaluating the implementation of non-, semi-, and fully immersive VR training for patient- or caregiver-facing communication skills training provided to graduate, medical, or other allied health care professions students were included. Studies that assessed augmented reality, mixed reality, artificial intelligence, or VR for non–communication-based training were excluded. Study selection will include a title, abstract, and full-text screening by 4 authors. Data from eligible studies will be extracted and entered into a database and presented in tabular format. Findings will be reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. RESULTS As of April 11, 2023, the search strategy has been confirmed and the search has been completed. We are currently at the title and abstract screening stage. Once complete, the articles will undergo full-text screening according to eligibility criteria as described in the methods. CONCLUSIONS The findings of this review will inform the development of a graduate-level clinical skills research course within the Institute of Medical Science graduate department at the University of Toronto. It is also expected that these findings will be of interest to other health care–specific faculties inside and beyond our institution. Further, our scoping review will summarize the limited field of literature on VR use in medical communications training and identify areas for future inquiry. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/53901
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.054 | 0.072 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".