Simulation Technology
Bibliographic record
Abstract
BACKGROUND: The availability of simulator technology at the University of Toronto (Toronto, Ontario, Canada) provided the opportunity to compare the efficacy of video-assisted and simulator-assisted learning. METHODS: After ethics approval from the University of Toronto, all final-year medical students were invited to participate in the current randomized trial comparing video-based to simulator-based education using three scenarios. After an introduction to the simulator environment, a 5-min performance-based pretest was administered in the simulator operating room requiring management of a critical event. A posttest was administered after students had participated in either a faculty-facilitated video or simulator teaching session. Standardized 12-point checklist performance protocols were used for assessment purposes. As well, students answered focused questions related to the educational sessions on a final examination. Student opinions regarding the value of the teaching sessions were obtained. RESULTS: One hundred forty-four medical students participated in the study (scenario 1, n = 43; scenario 2, n = 48; scenario 3, n = 53). There was a significant improvement in posttest scores over pretest scores in all scenarios. There was no statistically significant difference in scores between simulator or video teaching methods. There were no differences in final examination marks when the two educational methods were compared. Student opinions indicated that the experiential simulator sessions were more enjoyable and valuable than the video teaching sessions. CONCLUSIONS: Both simulator and video types of faculty-facilitated education offer a valuable learning experience. Future work is needed that addresses the long-term effects of experiential learning in the retention of knowledge and acquired skills.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".