‘You are shaken awake!’ a qualitative study on using virtual reality to understand patients’ experiences
Bibliographic record
Abstract
PURPOSE OF THE ARTICLE: Medical professionals may get to appreciate the experience of patients, without being patients themselves. This study explored whether VR technology can be useful for teaching medical professionals about patient experiences. MATERIALS AND METHODS: A VR movie was made, showing a fictitious but realistic patient experience. Nineteen medical professionals, selected by a non-probability sampling method, saw this movie. Focus group discussions and interviews were held afterwards. Data were thematically analyzed with independent coding. Codes and (sub)themes were discussed within the research team. RESULTS: Immediately after viewing, medical professionals felt 'shaken awake' and after several weeks, they maintained a heightened awareness of patient experiences in their daily practice. The VR movie was seen as a valuable teaching tool, despite the passive viewer role. The VR movie made them aware of three aspects of the doctor-patient relationship: a) paying attention, b) a person-centered approach, and c) building confidence. CONCLUSIONS: A VR movie helps medical professionals to 'see' and 'feel' what patients experience. The character in the VR movie is unable to respond to the fellow characters, underlining the passivity of patients. VR-glasses can be an easy way to encounter what others experience, although a blended learning approach, using passive patients' roles with VR-technology and active participation with role play methods, is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".