Attitudes towards sports injury and injury prevention among university athletes.
Bibliographic record
Abstract
Background: Adherence to injury prevention strategies remains a major challenge in the sports community despite such strategies having been proven effective in reducing the occurrence of injury. This may be because the behavioural and contextual factors of injury and injury prevention are not fully understood. Purpose: The study aimed to examine university athletes' attitudes toward sports injury and injury prevention and their perceived facilitators and barriers to injury prevention implementation. Methods: This study used a cross-sectional design and a survey as the data collection mode. Basketball, football, volleyball, soccer, and ice hockey athletes were recruited from the University of Manitoba and York University teams. Results: This study revealed three key findings: 1) There was a significant association between athletes’ history of injury and their attitudes toward injury and injury prevention, 2) university athletes’ attitudes toward injury differ from their attitudes toward injury prevention, and 3) the majority of the university athletes perceive long training programs, match congestion, and time constraints as barriers, and perceive short training programs, injury prevention education, free and good equipment, trained medical staff, performance-enhanced injury prevention program, and athletes motivation as facilitators to the implementation of injury prevention measures. Conclusion: Behavioural and educational intervention is needed to improve university athletes (especially those with a history of injury) attitudes toward injury and injury prevention. Also, other stakeholders in university sports need to work together and consider the perceived facilitators and barriers to injury prevention implementation to ensure adherence to injury prevention programs in real sports settings.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".