Relationship between Movement Competence and Degree of Sports Specialization in 8-to-12-year-old Football Players
Bibliographic record
Abstract
An increase in youth sport specialization prevalence has been associated with an increase injury rate and a decrease in movement competence. However, movement competence has not been compared between the degrees of sport specialization in 8- to 12-year-old football players. The purpose of the study is to primarily observe the relationship between movement competence and the degree of youth sports specialization in 8- to 12-year-old football players using the Child Focused Injury Risk Screening Tool (ChildFIRST). Secondly, the study aims to observe the differences amongst positions and the association for injury prevalence. We hypothesize that youth football players with a higher youth sport specialization categorization will have a lower movement competency. We also hypothesize that there with be a difference in movement competency amongst football positions. \n \nDuring practices in the 2023 football season, 8- to 12-year-old football players from the Montreal Regional Football League were asked to complete an injury and youth sport specialization questionnaire. Participants were then assessed using the ChildFIRST. There was no significant association between ChildFIRST composite score and youth sport specialization score. When looking at the differences amongst positions, linemen had a significantly lower ChildFIRST composite score mean than other positions. No association with injury and movement competence was observed. Future studies should continue observing the movement competency in 8- to 12-year-old football players differentiating by their playing position. Such findings could contribute towards the development of an evidence-based injury prevention program for youth football players.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".