Review of Simulation in Pediatrics: The Evolution of a Revolution
Bibliographic record
Abstract
Recent changes in medical education have highlighted the importance of experiential learning. Simulation is one model that has gained significant attention in the last decade and has been widely adopted as a training and assessment tool in medical education. Pediatric simulation has been utilized to teach various skills including resuscitation and trauma management, procedural skills, and team training. It is also a valuable tool for health care educators, as it allows learners to achieve competence without putting patients at risk. Recent literature demonstrates increased retention of knowledge and skills after simulation-based training. Further research is required to improve current simulation curriculums, develop validated assessment tools, and to demonstrate improved clinical outcomes after simulation-based training. We conducted an online search of original and review articles related to simulation and pediatric medical education and provide an overview of the role and utility of simulation in pediatrics. Key PointsSimulation in pediatrics has been widely accepted and adapted as a training and assessment tool in medical education.Simulation in pediatrics has been utilized to teach various skills including resuscitation and trauma management, procedural skills, and team training.Further research is required to improve current simulation curriculums, to develop validated assessment tools, and to demonstrate improved clinical outcomes after simulation-based training.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".