Development of an Emergency Medicine Simulation Fellowship Consensus Curriculum: Initiative of the Society for Academic Emergency Medicine Simulation Academy
Bibliographic record
Abstract
OBJECTIVES: There is currently no consolidated list of existing simulation fellowship programs in emergency medicine (EM). In addition, there are no universally accepted or expected standards for core curricular content. The objective of this project is to develop consensus-based core content for EM simulation fellowships to help frame the critical components of such training programs. METHODS: This paper delineates the process used to develop consensus curriculum content for EM simulation fellowships. EM simulation fellowship curricula were collected. Curricular content was reviewed and compiled by simulation experts and validated utilizing survey methodology, and consensus was obtained using a modified Delphi methodology. RESULTS: Fifteen EM simulation fellowship curricula were obtained and analyzed. Two rounds of a modified Delphi survey were conducted. The final proposed core curriculum content contains 47 elements in nine domains with 14 optional elements. CONCLUSION: The proposed consensus content will provide current and future fellowships a foundation on which to build their own specific and detailed fellowship curricula. Such standardization will ultimately increase the transparency of training programs for future trainees and potential employers.
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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 | 0.046 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".