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Record W7117483759 · doi:10.1007/s40670-025-02604-4

Virtual Reality for Developing Patient-facing Communication Skills in a Medical Science Graduate Education Course: A Mixed-Methods Pre-Post Study

2025· article· en· W7117483759 on OpenAlexafffund
Kyla Gaeul Lee, Maryam Sorkhou, Nicole Harnett, Sobiga Vyravanathan, Theodore J. Brown, Evan Tannenbaum, Nairy Khodabakhshian

Bibliographic record

VenueMedical Science Educator · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsHospital for Sick ChildrenLunenfeld-Tanenbaum Research InstituteCentre for Addiction and Mental HealthPrincess Margaret Cancer CentreSinai Health SystemUniversity of Toronto
FundersTemerty Faculty of Medicine, University of Toronto
KeywordsPreparednessFocus groupCommunication skillsInformed consentQualitative researchClass (philosophy)Sample (material)Qualitative property

Abstract

fetched live from OpenAlex

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.

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

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.074
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.074
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0040.003
Scholarly communication0.0000.001
Open science0.0040.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.157
GPT teacher head0.566
Teacher spread0.409 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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