Foundational Elements of Applied Simulation Theory: Development and Implementation of a Longitudinal Simulation Educator Curriculum
Bibliographic record
Abstract
Simulation-based education has gained popularity, yet many faculty members feel inadequately prepared to teach using this technique. Fellowship training in medical education exists, but there is little information regarding simulation or formal educational programs therein. In our institution, simulation fellowships were offered by individual clinical departments. We recognized the need for a formal curriculum in educational theory. Kern's approach to curriculum development was used to develop, implement, and evaluate the Foundational Elements of Applied Simulation Theory (FEAST) curriculum. Needs assessments resulted in a 26-topic curriculum; each biweekly session built upon the previous. Components essential to success included setting goals and objectives for each interactive session and having dedicated faculty, collaborative leadership and administrative support for the curriculum. Evaluation data was collated and analyzed annually via anonymous feedback surveys, focus groups, and retrospective pre-post self-assessment questionnaires. Data collected from 32 fellows over five years of implementation showed that the curriculum improved knowledge, challenged thinking, and was excellent preparation for a career in simulation-based medical education. Themes arising from focus groups demonstrated that participants valued faculty expertise and the structure, practicality, and content of the curriculum. We present a longitudinal simulation educator curriculum that adheres to a well-described framework of curriculum development. Program evaluation shows that FEAST has increased participant knowledge in key areas relevant to simulation-based education and that the curriculum has been successful in meeting the needs of novice simulation educators. Insights and practice points are offered for educators wishing to implement a similar curriculum in their institution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".