Knowledge transfer and retention of simulation-based learning for neurosurgical instruments: a randomised trial of perioperative nurses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it