Study to assess the need for paediatric trauma training in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it