The perceptions of professional soccer players on the risk of injury from competition and training on natural grass and 3rd generation artificial turf
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe professional soccer players' perceptions towards injuries, physical recovery and the effect of surface related factors on injury resulting from soccer participation on 3rd generation artificial turf (FT) compared to natural grass (NG). METHODS: Information was collected through a questionnaire that was completed by 99 professional soccer players from 6 teams competing in Major League Soccer (MLS) during the 2011 season. RESULTS: The majority (93% and 95%) of the players reported that playing surface type and quality influenced the risk of sustaining an injury. Players believed that playing and training on FT increased the risk of sustaining a non-contact injury as opposed to a contact injury. The players identified three surface related risk factors on FT, which they related to injuries and greater recovery times: 1) Greater surface stiffness 2) Greater surface friction 3) Larger metabolic cost to playing on artificial grounds. Overall, 94% of the players chose FT as the surface most likely to increase the risk of sustaining an injury. CONCLUSIONS: Players believe that the risk of injury differs according to surface type, and that FT is associated with an increased risk of non-contact injury. Future studies should be designed prospectively to systematically track the perceptions of groups of professional players training and competing on FT and NG.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".