A Decade Later—Progress and Next Steps for Pediatric Simulation Research
Bibliographic record
Abstract
SUMMARY STATEMENT: A decade ago, at the time of formation of the International Network for Pediatric Simulation-based Innovation, Research, and Education, the group embarked on a consensus building exercise. The goal was to forecast the facilitators and barriers to growth and maturity of science in the field of pediatric simulation-based research. This exercise produced 6 domains critical to progress in the field: (1) prioritization, (2) research methodology and outcomes, (3) academic collaboration, (4) integration/implementation/sustainability, (5) technology, and (6) resources/support/advocacy. This article reflects on and summarizes a decade of progress in the field of pediatric simulation research and suggests next steps in each domain as we look forward, including lessons learned by our collaborative grass roots network that can be used to accelerate research efforts in other domains within healthcare simulation science.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".