Simulation training improves resident physicians’ confidence in managing first trimester bleeding in the emergency department
Bibliographic record
Abstract
First trimester bleeding is commonly encountered in pregnancy and can be potentially life-threatening. Simulation training provides an ideal opportunity for resident medical learners to improve clinical knowledge and gain confidence in managing life-threatening causes of first trimester bleeding in a realistic but safe clinical environment. The objective of this study was to assess the effectiveness of simulation in improving family medicine residents’ confidence and knowledge in identifying and managing first trimester bleeding in the emergency department (ED). The intervention was a two-hour educational simulation focusing on management of unstable first trimester bleeding. Twenty-one family medicine residents (67% female) at the University of Toronto participated in the simulation and completed pre- and post-simulation questionnaires assessing their confidence and knowledge in management of first trimester bleeding. This study demonstrates that simulation training improves resident physicians’ confidence in managing first trimester bleeding in the ED. Additionally, it improves their objective history taking skills. Post-graduate medical programs should consider developing structured simulation, particularly for high-yield clinical cases residents may not otherwise have acute exposure to and are required to be competent in managing.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".