Experiencing COVID-19 Through the Patient Lens to Promote Empathy: Pilot Testing a Virtual Reality Learning Opportunity
Bibliographic record
Abstract
Understanding the patient's experience with COVID-19 was essential to providing high-quality, person-centered care during the pandemic. Having empathy or being able to understand and respond to the patient's experience may lead to improved outcomes for both patients and clinicians. There is mixed evidence about how best to teach empathy, particularly related to promoting empathy during COVID-19. Literature suggests that virtual reality may be effective in empathy-related education. In collaboration with four patient partners with lived experience, a 360° VR video was developed reflecting their stories and interactions with the healthcare system. The aim of this study was to pilot test the video with interprofessional healthcare providers (HPs) to explore acceptability and utility, while also seeking input on opportunities for improvement. Eleven HPs reviewed the video and participated in one of three focus groups. Focus group data were analyzed using thematic analysis. Data suggest that video content is acceptable and useful in promoting a better understanding of the patient's experience. Building on these encouraging findings, additional iterations of videos to promote empathy will be developed and tested.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".