The effect of coach and player injury knowledge, attitudes and beliefs on adherence to the FIFA 11+ programme in female youth soccer
Bibliographic record
Abstract
BACKGROUND: Injury knowledge and beliefs influence uptake of prevention programmes, but the relationship between knowledge, beliefs and adherence remains unclear. AIM: To describe injury knowledge and beliefs among youth female soccer coaches and players, and to identify the relationship between these factors, different delivery strategies of the FIFA 11+ programme and adherence. METHODS: A subcohort analysis from a cluster-randomised controlled trial of 31 female soccer teams (coaches n=29, players (ages 13-18) n=258). Preseason and postseason questionnaires were used to assess knowledge and beliefs. Teams recorded FIFA 11+ adherence during the season. RESULTS: At baseline, 62.8% (95% CI 48.4% to 77.3%) of coaches and 75.8% (95% CI 71.5% to 80.1%) of players considered 'inadequate warm-up' a risk factor for injury. There was no effect of delivery method (OR=1.1; 95% CI 0.8 to 1.5) or adherence (OR=1.0; 95% CI 0.9 to 1.1) on this belief. At baseline, 13.8% (95% CI 1.3% to 26.4%) of coaches believed a warm-up could prevent muscle injuries, but none believed it could prevent knee and ankle injuries. For players, 9.7% (95% CI 6.1% to 13.3%), 4.7% (95% CI 2.1% to 7.3%) and 4.7% (95% CI 2.1% to 7.3%) believed a warm-up would prevent muscle, knee and ankle injuries, respectively. Years of playing experience were negatively associated with high adherence for coaches (OR=0.93; 0.88 to 0.99) and players (OR=0.92; 0.85 to 0.98). CONCLUSIONS: There were gaps in injury knowledge and beliefs, which differed for coaches and players. Beliefs did not significantly affect adherence to the FIFA 11+, suggesting additional motivational factors should be considered.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".