An Immersive Virtual Reality Simulation Scenario to Improve Empathy in Nursing Students
Bibliographic record
Abstract
Empathy is essential in nursing practice and can be taught throughout nursing curriculum using a variety of methods including clinical experiences, in-person simulation, virtual reality, and didactic lecture. Empathy can also change over time, often decreasing the longer nurses practice. A cohort of upper-level nursing students viewed a short immersive virtual reality simulation as part of routine curriculum and completed the Toronto Empathy Questionnaire before viewing (time 1), 2 weeks later (time 2), and, for a small cohort, several months later (time 3). The sample included 110 undergraduate nursing students. There were no improvements in Toronto Empathy Questionnaire scores from time 1 to time 2. There was no improvement from time 1 to time 3 for the cohort who completed the Toronto Empathy Questionnaire three times. There were no significant differences in Toronto Empathy Questionnaire scores between cohorts for any measurement times. Total mean empathy scores were comparatively high in this study and did not decline over time. Although this virtual reality simulation scenario appears to have protected against decline in empathy, it may have been insufficient to foster an increase in empathy scores. Empathic training needs to be immersed throughout their nursing education in both didactic and clinical settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".