Study to assess the need for paediatric trauma training in India
Bibliographic record
Abstract
INTRODUCTION: Paediatric trauma is an ever-rising problem in low- and middle-income (LMIC) countries. Recent studies have demonstrated that children with lower injury scores are more likely to die in LMIC countries as compared to developed countries. We conducted this study to assess the need for a dedicated trauma training curriculum relevant to the epidemiology of paediatric injuries in LMIC countries. METHODS: We conducted the study in the apex trauma training site in India, wherein a predesigned questionnaire was circulated to understand the need for additional trauma training for children. RESULTS: A total of 642 trauma care providers out of 800 (response rate is 80.25%) completed the study. Eighty-six per cent (552/642) of trauma care providers felt the need for paediatric trauma training. Only 40% (255/642) of trauma providers were confident in handling children. CONCLUSION: In an anonymous survey, trauma care providers in India admit that they need more specific paediatric trauma training because the majority of them are not confident in handling child victims of trauma. Furthermore, they felt the best solution would be to create paediatric trauma centres, instead of caring for children in adult centres for traumas. Further studies are needed to discover if the development of a standardized Paediatric Trauma Resuscitation Module for trauma care providers can increase their confidence in caring for children who are victims of road injury or other traumas in low- and middle-income countries, and if specialized paediatric trauma centres would indeed decrease morbidity and mortality of children who experience trauma in LMIC countries.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".