Learning to Manage COVID-19-induced Respiratory Distress in the Immersive Virtual Reality Simulation: A Pre-Experimental Study
Bibliographic record
Abstract
Introduction: Undergraduate nursing students have limited theoretical knowledge of and practical learning opportunities to manage COVID-19 respiratory distress. Immersive virtual reality simulations in nursing represent a new area of educational interest that has been understudied in Canada. Objective: This study aimed to measure the potential impact of immersive virtual reality simulation on content knowledge in respiratory distress induced by COVID-19, as well as perceived learning and perceived confidence in managing this condition among senior undergraduate nursing students. Methods: A pre-experimental design with a one-group pretest-post-test was employed. Nursing students (n = 30) were recruited through convenience sampling to participate in a single immersive virtual reality simulation session. Data were collected using the Respiratory Distress Management Knowledge Test, the Simulation Effectiveness Tool-Modified (subscales: Learning and Confidence), and an open-ended question. Results: The results showed an increase in knowledge (p=0.01). Participants reported a high perception of learning and confidence, and shared that this simulation helped them identify areas for improvement and strengthened their existing skills. Discussion and Conclusion: These results suggest that this immersive virtual reality simulation has the potential to enhance students’ knowledge about respiratory distress. Subjective evaluations highlight its educational potential. Further studies could explore simulation’s impact on nursing students and professionals by integrating a control group and other validated measures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".