Training the trainers: a survey of simulation fellowship graduates
Bibliographic record
Abstract
BACKGROUND: Coupled with the expansion of simulation has been the development and growth of medical simulation fellowships. These non-accredited fellowships do not have a standardized curriculum and there are currently no studies investigating the simulation fellowship experience. The purpose of this study was to explore the simulation fellowship experience of graduates throughout North America and how it prepared them for their post-fellowship career. METHODS: A web-based survey was developed by Emergency Medicine attending physicians both of whom completed one-year fellowships in medical simulation. Prior to distribution, the survey was reviewed and tested by three simulation fellowship graduates and a PhD researcher. Feedback was integrated into the survey prior to distribution. The survey consisted of a maximum of 29 multiple choice questions including two step-logic questions and two open response questions. The survey was distributed to simulation fellowship directors in multiple disciplines and the directors were asked to forward the survey to graduates. Additionally, the Society for Academic Emergency Medicine Simulation Academy list-serve was utilized for distribution of the survey. RESULTS: The survey had 35 responses. The majority of respondents completed fellowship within the last two years (66%, 23/35). Fellowship graduates strongly agreed or agreed that their fellowship adequately prepared them for their post-fellowship simulation career (88%). Graduates report that research design/reporting (53%) and administration (18%) were areas of their fellowship curriculum that needed the most improvement. CONCLUSION: The majority of simulation fellowship graduates agreed that their fellowship experience adequately prepared them for their post-fellowship simulation career. Graduates also felt that training in research and administration are areas that could be improved.
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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 | 0.003 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".