Characteristics and Core Curricular Elements of Medical Simulation Fellowships in North America
Bibliographic record
Abstract
Background In the past few years, there has been rapid growth in the number of simulation fellowships for physicians in the United States and Canada, with the objective of producing faculty with expertise and leadership training in medical simulation. Relatively little is known about the collective content and structure of these new fellowship opportunities. Objective We sought to identify a common set of core curricular elements among existing simulation fellowships and to obtain demographic background information on participants and leadership. Methods We designed a web-based survey and circulated it to simulation fellowship directors in the United States and Canada. The questions explored aspects of the fellowship curriculum. A grounded theory approach was used to qualitatively analyze fellowship goals and objectives. Results Of the 29 program directors surveyed, 23 responded (79%). The most commonly listed goals and objectives were to increase skills in simulation curriculum development, simulation operations and training environment setup, research, educational theory, administration, and debriefing. The majority of the responding fellowship directors (17 of 22, 77%) indicated that a set of consensus national guidelines would benefit their fellowship program. Conclusions Simulation fellowships are experiencing a period of rapid growth. Development of a common set of program guidelines is a widely shared objective among fellowship directors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".