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Record W4225157687 · doi:10.2196/32657

Perspectives of 360-Degree Cinematic Virtual Reality: Interview Study Among Health Care Professionals

2022· article· en· W4225157687 on OpenAlexvenueno aff
Elizabeth A. Beverly, Brooke Rigot, Carrie Love, Matthew Love

Bibliographic record

VenueJMIR Medical Education · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
FundersOsteopathic Heritage Foundation
KeywordsThematic analysisVirtual realityMedical educationHealth careNarrativePsychologyQualitative researchNursingMedicineComputer scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The global market for medical education is projected to increase exponentially over the next 5 years. A mode of delivery expected to drive the growth of this market is virtual reality (VR). VR simulates real-world objects, events, locations, and interactions in 3D multimedia sensory environments. It has been used successfully in medical education for surgical training, learning anatomy, and advancing drug discovery. New VR research has been used to simulate role-playing and clinical encounters; however, most of this research has been conducted with health professions students and not current health care professionals. Thus, more research is needed to explore how health care professionals experience VR with role-playing and clinical encounters. OBJECTIVE: The aim of this study was to explore health care professionals' experiences with a cinematic VR (cine-VR) training program focused on role-playing and clinical encounters addressing social determinants of health, Appalachian culture, and diabetes. Cine-VR leverages 360-degree video with the narrative storytelling of cinema to create an engaging educational experience. METHODS: We conducted in-depth telephone interviews with health care professionals who participated in the cine-VR training. The interviews were audio recorded and transcribed verbatim. A multidisciplinary team coded and analyzed the data using content and thematic analyses with NVivo software. RESULTS: We conducted 24 in-depth interviews with health care professionals (age=45.3, SD 11.3, years; n=16, 67%, women; n=22, 92%, White; and n=4, 17%, physicians) to explore their experiences with the cine-VR training. Qualitative analysis revealed five themes: immersed in the virtual world: seeing a 360-degree sphere allowed participants to immerse themselves in the virtual world; facilitated knowledge acquisition: all the participants accurately recalled the culture of Appalachia and listed the social determinants of health presented in the training; empathized with multiple perspectives: the cine-VR provided a glimpse into the real life of the main character, and participants described thinking about, feeling, and empathizing with the character's frustrations and disappointments; perceived ease of use of cine-VR: 96% (23/24) of the participants described the cine-VR as easy to use, and they liked the 360-degree movement, image resolution, and sound quality but noted limitations with the buttons on the headsets and risk for motion sickness; and perceived utility of cine-VR as a teaching tool: participants described cine-VR as an effective teaching tool because it activated visual and affective learning for them. CONCLUSIONS: Participants emphasized the realism of the cine-VR training program. They attributed the utility of the cine-VR to visual learning in conjunction with the emotional connection to the VR characters. Furthermore, participants reported that the cine-VR increased their empathy for people. More research is needed to confirm an association between the level of immersion and empathy in cine-VR training for health care professionals.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.415
Teacher spread0.359 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations24
Published2022
Admission routes1
Has abstractyes

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