Simulation curriculum evaluation and development in a postgraduate emergency medicine programme
Bibliographic record
Abstract
Simulation is a technique that holds the most value when used as an effective learning tool by trained individuals.1 Features of high-fidelity simulation that promote learning include feedback, repetition, individualisation of cases, variation of difficulty and conduction of clinical scenarios in a controlled environment.2 Having regular simulation-based educational (SBE) activities leads to skill acquisition that is transferable to real-life situations.2 Emergency medicine (EM) residents at the University of British Columbia (UBC) in Canada have a variety of SBE opportunities across the four main training sites (Vancouver, New Westminster, Victoria and Kelowna). These include junior and senior resident laboratory-based SBE on a monthly basis, a first-year resident procedural skills training day and in situ simulation conducted in the emergency department at varying intervals depending on the site. While EM residents at UBC have regular time dedicated to participating in SBE, there is variability in the delivery of the education with regard to format, facilitation, case difficulty and debriefing. A 2017 Canadian national survey regarding simulation curricula in postgraduate EM programmes found that 94% of programmes have a simulation curriculum.3 Even so, we do not know exactly what these curricula are made up of. Using Kern’s six-step model for curriculum development,4 we set out to complete step two …
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".