Bibliographic record
Abstract
BACKGROUND: Simulation training is increasingly being used as part of the undergraduate medical curriculum, but it remains time and faculty member intensive. To improve efficacy, videos have been used prior to the simulation of practical procedures; however, using videos prior to simulation training concerning the management of patients who are unwell has not been investigated. The aim of this project was to see whether clinical decision-making and non-technical skills can be improved if a video is used prior to simulation training, and uniquely to enhance the authenticity we filmed it using a first-person perspective. METHODOLOGY: We conducted a randomised controlled trial with 40 final-year medical students randomised into two groups. One group viewed a video filmed in first person prior to a simulation scenario, whereas the other group did not view the video. The two groups then carried out the simulation and were assessed with 'time to' investigation and treatment decisions. Further quantitative data were collected for non-technical skills using the Ottawa Crisis Resource Management (OCRM) score. Qualitative data were collected from the students as to the perceived ease of use and helpfulness of the video. Simulation training is increasingly being used as part of the undergraduate medical curriculum RESULTS: The students who watched the video appeared to perform better in clinical decision-making and non-technical skills. The students were extremely receptive to the use of a first-person perspective video, and highlighted its perceived realism and its help as a memory aid. DISCUSSION: The use of this style of video was warmly received by students and opens the possibility of further use to enhance simulation training.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".