Simulation curriculum evaluation and development in a postgraduate emergency medicine programme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it