Feasibility of scenario-based simulation training versus traditional workshops in continuing medical education: a randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Although simulation-based training is increasingly used for medical education, its benefits in continuing medical education (CME) are less established. This study seeks to evaluate the feasibility of incorporating simulation-based training into a CME conference and compare its effectiveness with the traditional workshop in improving knowledge and self-reported confidence. METHODS: Participants (N=27) were group randomized to either a simulation-based workshop or a traditional case-based workshop. RESULTS: Post-training, knowledge assessment score neither did increase significantly in the traditional group (d=0.13; p=0.76) nor did significantly decrease in the simulation group (d= - 0.44; p=0.19). Self-reported comfort in patient assessment parameters increased in both groups (p<0.05 in all). However, only the simulation group reported an increase in comfort in patient management (d=1.1, p=0.051 for the traditional group and d=1.3; p= 0.0003 for the simulation group). At 1 month, comfort measures in the traditional group increased consistently over time while these measures in the simulation group increased post-workshop but decreased by 1 month, suggesting that some of the effects of training with simulation may be short lived. DISCUSSION: The use of simulation-based training was not associated with benefits in knowledge acquisition, knowledge retention, or comfort in patient assessment. It was associated with superior outcomes in comfort in patient management, but this benefit may be short-lived. Further studies are required to better define the conditions under which simulation-based training is beneficial.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".