Bibliographic record
Abstract
Medical simulation is an effective method to teach high-risk procedural skills, identify latent safety threats in healthcare, improve patient safety, and develop teamwork and communication skills. As the field of medical simulation continues to grow rapidly, fellowship training in medical simulation also continues expanding to meet the growing demand. In only ten years, over 45 new simulation fellowships have started worldwide. With increased utilization of medical simulation in training, there is an associated increase in demand for well-trained, effective simulation educators. Simulation fellowships exist to provide this training and generate graduates who are successful in administrative skills required to operate a simulation center, effectively facilitate and debrief learners, design curricula to achieve educational objectives, and publish simulation-based research to further the specialty.The rapid expansion of simulation fellowships has led to a lack of standardization in the fellowship curriculum. While this allows for tailored training toward trainee interest, it also creates wide variability in the curriculum and potentially limits the transferability of fellowship training. Medical simulation fellowships have not obtained accreditation from the Accreditation Council on Graduate Medical Education (ACGME) or Royal College of Physicians and Surgeons of Canada (RCPSC). Surgical simulation fellowships do have accreditation from the American College of Surgery. The content and structure of medical simulation fellowships vary, as evidenced by previous studies surveying fellowship program directors and graduates.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.043 |
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".