The development of a national pediatric trauma curriculum
Bibliographic record
Abstract
BACKGROUND: Educational programs dedicated to pediatric trauma are either not available or comprehensive. Pediatric trauma is thus managed by a range of specialists with training in a variety of related fields. Post-certification fellowships in pediatric medicine all mandate education in the assessment and management of the injured child. The purpose of this study was to develop a blueprint for a national pediatric trauma training curriculum. METHODS: A team of four experts developed content for a national pediatric trauma curriculum and disseminated it to 11 pediatric trauma sites across Canada. The objectives contained both knowledge and skill sets related to the management of the pediatric trauma patients. A multi-tiered Delphi process was used to develop the final content. RESULTS: All the 11 pediatric teaching centers across the country participated. A final list, representing a consensus of views, was developed in 10 domains through the iterative process of the Delphi technique. The domains for the curriculum included introduction to pediatric trauma and epidemiology, initial management, pediatric airway, shock, thoracic injuries, abdominal and pelvic injuries, spinal and neurological injuries, pediatric head injuries, burns and electrical injuries, and orthopedic injuries. CONCLUSION: The Delphi process is an invaluable tool in developing curricula. The pediatric trauma curriculum can be used in teaching hospitals for house staff education and meeting core competencies. The blueprint can be validated further in the future.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".