Virtual Reality for Developing Patient-facing Communication Skills in a Medical Science Graduate Education Course: A Mixed-Methods Pre-Post Study
Bibliographic record
Abstract
Abstract Communication skills are essential for patient-centered clinical research, yet traditional teaching methods offer limited opportunities for trainees to strengthen this competency. This study evaluates the impact of virtual reality (VR) modules on enhancing communication skills among graduate research trainees in medical science. A mixed-methods pre-post design was used to triangulate quantitative and qualitative data. Pre- and post-course scores for readiness and knowledge (Winter: n = 11, Fall: n = 29) were analyzed using a paired sample t-test. Qualitative data were collected during a class debriefing. Our quantitative findings revealed significant improvements in post-course scores for both knowledge ( p < .001) and readiness for clinical integration ( p < .05) compared to pre-course scores. Qualitatively, students described the modules as realistic, immersive, and engaging. However, they faced challenges in distinguishing their roles as researchers versus clinicians and in addressing cultural nuances during informed consent. VR-based learning improved students’ confidence and preparedness for real-world clinical research. Findings suggest the need for comprehensive education on informed consent and a stronger focus on ethical communication and culturally safe research practices. The modules also encouraged deep self-reflection, prompting students to confront their biases and their impact on participant inclusion.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".