32 Blending the old with the new: “see one, do one”: a randomised controlled trial into the use of a first person perspective simulation video prior to a simulation scenario
Bibliographic record
Abstract
Introduction Simulation training is increasingly seen as an integral part of the medical curriculum. However it is time and faculty intensive and more efficient uses of it are being sought. One solution could be to employ the old adage of “see one, do one”. Studies have measured benefits of videos shown prior to learning practical procedures. However no work has been done into benefits of videos shown prior to simulation scenarios. Methods We produced a video of the management of pulmonary oedema utilising a first person perspective to improve authenticity and realism. Thirty seven students were randomised with seventeen viewing the video prior to simulation and twenty not. The students then carried out a simulation on the management of pulmonary oedema. They were scored on “time to decision” of diagnosis, investigation and management and non-technical skills using the Ottawa Crisis Resource Management (CRM) score. Results Students who watched the video were better in all scored domains. 100% of students who watched the video made the correct diagnosis compared to 70% of those who had not. They were significantly faster at making the correct diagnosis (p = 0.01), and in almost all “time to decisions” for investigation and management the test arm appeared to perform better than the control arm. The students’ non-technical skills were also better with a significant improvement in four out of the five domains of the CRM score. Discussion Students appeared to perform globally better but in particular with correct diagnosis and non-technical skills. Conclusion The use of a pre simulation video as a teaching tool appears to improve students’ performance in clinical decision making and non-technical skills. Combining a pre-simulation video (“see one”), with simulation (“do one”) appears to improve students’ performance in clinical scenarios.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".