Female soccer knee injury: Observed knowledge gaps in injury prevention among players/parents/coaches and current evidence (the <scp>KNOW</scp> study)
Bibliographic record
Abstract
This study sought to determine if knowledge regarding the risk for knee injuries and the potential for their prevention is being translated to female adolescent soccer players (13-18 years), their parents, and coaches. Eligible participants in the 2007 indoor soccer season were surveyed to determine their knowledge of the risk for and the potential to prevent knee injuries, and their knowledge of effective prevention strategies, if they felt that injury prevention was possible. Team selection was stratified to be representative of both competitive and recreational level play and age group distributions within the selected soccer association. Of the study subjects, 773/1396 (55.4%) responded to the survey: 408 (53%) players, 292 (38%) parents, and 73 (9%) coaches. Most respondents (538 [71%]) were aware of the risk for knee injury. Coaches and parents were more likely than players to view knee injuries as preventable; however, appropriate prevention strategies were often not identified. Four hundred eighty-four (63.8%) respondents reported that they had never received information on knee injuries. Substantial knowledge gaps regarding knee injury prevention and effective preventative strategies were identified. Given the predominance of knee injuries in female adolescent soccer players, there is an urgent need for knowledge translation of prevention strategies to decrease both incidence and long-term consequences of knee injuries.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".