Injury prevention in paediatric sport-related injuries: a scientific approach
Bibliographic record
Abstract
Youth have very high participation rates in sport, and sport is the leading cause of youth injury in many countries.[1][2][3][4][5][6][7] Canadian studies report that 30-40% of youth (ages 11-18 years) seek medical attention for a sport injury annually.2 7 While physical activity prevents all-cause morbidity associated with a sedentary lifestyle, injuries can become a barrier to physical activity.Injury prevention in youth is a critical issue in healthcare and in the promotion of health and wellness in our communities, and is becoming a public health priority.[8][9] However, there is a discrepancy between the amount of research in this area and the public health burden of injury in youth sport where injuries are often predictable and preventable.10 An interdisciplinary and rigorous scientific approach is critical to understanding the complexity of injury risks, prevention and safety policies related to sport injury in youth.Studies that examine prevention strategies are paramount in establishing best practices for prevention in youth sport for healthcare practitioners, sport and health administrators, policy makers, athletes, coaches, parents and the public.The results of research in this area are often pivotal in decisions made to continue, discontinue, allocate or reduce funds from given public health, sport and healthcare programmes.As such, a rigorous methodological approach to research in injury prevention in youth sport is essential to inform practice and policy most appropriately.The purpose of this paper is to advocate for a scientific approach to research in injury prevention in child and adolescent sport.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".