Knowledge transfer and retention of simulation-based learning for neurosurgical instruments: a randomised trial of perioperative nurses
Bibliographic record
Abstract
Introduction: Previous studies have shown that simulation is an acceptable method of training in nursing education. The objectives of this study were to determine the effectiveness of tablet-based simulation in learning neurosurgical instruments and to assess whether skills learnt in the simulation environment are transferred to a real clinical task and retained over time. Methods: A randomised controlled trial was conducted. Perioperative nurses completed three consecutive sessions of a simulation. Group A performed simulation tasks prior to identifying real instruments, whereas Group B (control group) was asked to identify real instruments prior to the simulation tasks. Both groups were reassessed for knowledge recall after 1 week. Results: Ninety-three nurses completed the study. Participants in Group A, who had received tablet-based simulation, were 23% quicker in identifying real instruments and did so with better accuracy (93.2% vs 80.6%, p<0.0001) than Group B. Furthermore, the simulation-based learning was retained at 7 days with 97.8% correct instrument recognition in Group A and 96.2% in Group B while maintaining both speed and accuracy. Conclusion: This is the first study to assess the effectiveness of tablet-based simulation training for instrument recognition by perioperative nurses. Our results demonstrate that instrument knowledge acquired through tablet-based simulation training results in improved identification and retained recognition of real instruments.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".