Using Virtual Reality for Perioperative Nursing Education in Complex Neurosurgical Surgeries: A Feasibility and Acceptance Study
Bibliographic record
Abstract
Background Operating room (OR) nurses' training for surgical fields such as neurosurgery is often inconsistent and overly lengthy due to the lack of consistently scheduled procedures and the nature of procedures being for the most part emergencies. Virtual reality (VR) simulation has been explored for nurses training in various contexts with positive results. Objectives To develop a VR simulation that could replicate a pediatric neurosurgery craniotomy procedure reflecting a real OR scenario and the surgical procedural sequence of a craniotomy; and to assess OR nurses' confidence in assisting craniotomy procedures as scrub nurses before and after the VR simulation. Methods A pediatric craniotomy procedure was replicated using VR technology by a collaborative partnership between education, content, and technology experts within the Hospital for Sick Children, Toronto. Self-confidence among OR nurses to assist in craniotomy procedures was explored pre- and post-VR training sessions with a questionnaire ideated by the authors evaluating knowledge relevant to assisting craniotomy procedures with seven items. Results In total, 7 OR nurses participated in the study. The post-VR sessions questionnaires showed an increase of positive answers "extremely comfortable with the procedure" and "moderately comfortable with the procedure" compared to pre-VR sessions in all items except for "identify the hemostatic agents required during a bleed," for which no difference was noted. There were no issues with the equipment. Conclusion VR simulation session is an acceptable model to train OR nurses for the scrub nurse role in craniotomy procedures. VR simulation is a practical learning strategy for clinical situations that may occur inconsistently in real-time practice.
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 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".