Lower limb injury prevention programs in youth soccer: a survey of coach knowledge, usage, and barriers
Bibliographic record
Abstract
BACKGROUND: Participation in youth soccer carries a significant risk of injury, most commonly non-contact injuries of the lower extremity. A growing body of research supports the use of neuromuscular interventions by teams to prevent such injuries, yet the uptake of these recommendations by soccer teams remains largely unexplored. The purposes of the study were to determine (1) the level of awareness by youth coaches of injury prevention programs and their efficacy; (2) the number of youth coaches that use these interventions; and (3) barriers and potential facilitators to implementing a sustainable injury prevention program. METHODS: Four hundred eighteen coaches of male and female youth soccer teams were emailed an online blinded survey. This survey consisted of 26 questions covering coaches' demographics, level of training, experience with injuries among players, and use of injury prevention programs. Question development was guided by the RE-AIM Sports Setting Matrix in combination with findings from the literature review and expert experience from orthopaedic surgeons specializing in sport medicine. RESULTS: Of the 418 coaches contacted, 101 responded. Only 29.8% of respondents used an injury prevention program in the prior soccer season. Coaches that had completed one or more coaching courses were more likely to use an intervention. Of those that did not already use an intervention, coaches agreed or strongly agreed that they would consider using one if it could be used in place of the warm up and take no more than 20 min (74.0%), if they could access information about the exercises (84.0%), and if the exercises could be properly demonstrated (84.0%). Additionally, 84% of coaches that did not already use an intervention agreed or strongly agreed that knowing that interventions may reduce a player's risk of injury by 45% would affect whether they would use one. CONCLUSION: This study suggests that the current use and awareness of injury prevention programs is limited by a lack of communication and education between sporting associations and coaches, as well as perceived time constraints. The results also suggest that improving coaching education of injury prevention could increase the frequency of intervention use.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".