Simulation in Neonatal-Perinatal Medicine Fellowship Programs
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the use of simulation in neonatal-perinatal medicine (NPM) fellowship programs. STUDY DESIGN: This was a cross-sectional survey of program directors (PDs) and simulation educators in Accreditation Council for Graduate Medical Education (ACGME) accredited NPM fellowship programs. RESULTS: Responses were received from 59 PDs and 52 simulation educators, representing 60% of accredited programs. Of responding programs, 97% used simulation, which most commonly included neonatal resuscitation (94%) and procedural skills (94%) training. The time and scope of simulation use varied significantly. The majority of fellows (51%) received ≤20 hours of simulation during training. The majority of PDs (63%) wanted fellows to receive >20 hours of simulation. Barriers to simulation included lack of faculty time, experience, funding, and curriculum. CONCLUSION: While the majority of fellowship programs use simulation, the time and scope of fellow exposure to simulation experiences are limited. The creation of a standardized simulation curriculum may address identified barriers to simulation.
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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 | 0.004 | 0.025 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".