<i>To sim or not to sim</i>—choosing wisely for procedural skills training in paediatrics
Bibliographic record
Abstract
Objectives: Simulation is a commonly used modality to teach paediatric procedural skills, however, it is resource intensive. Which paediatric skills are best taught using simulation is not known. This study aims to examine what skills to simulate, allowing for the best use of resources in ever expanding curricula. Method: We administered a questionnaire to community and hospitalist general paediatricians in Canada asking them to rate the importance of maintaining competency in each paediatric procedural skill and the frequency with which they perform the skill in their practice. Results: Skills that were rated as highly important and also high frequency were: bag-mask ventilation (BMV), lumbar puncture, neonatal cardiopulmonary resuscitation (CPR), specimen procurement for infectious diseases, immunization, and ear curettage. Skills that were rated as highly important but low frequency were: paediatric CPR, intraosseous needle insertion, neonatal intubation, defibrillation, gathering specimens for evidence of child maltreatment, paediatric intubation, cervical spine immobilization, and oral/nasogastric tube placement. Conclusion: Paediatric procedural skills are rated variably in terms of importance and frequency of use in general paediatric practice. Eight skills of high importance are infrequently performed and should be targeted for teaching via 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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".