Analyzing injuries among university-level athletes: prevalence, patterns and risk factors.
Bibliographic record
Abstract
BACKGROUND: Scientific evidence suggests many health benefits are associated with sport participation. However, high intensity participation may be related to an increased risk of musculoskeletal injuries. OBJECTIVES: This study aims to: 1) describe the prevalence and patterns of sports injuries, and 2) identify its associated risk factors. METHODS: A cross-sectional design was used. University level athletes, involved in 7 sport disciplines reported musculoskeletal injuries sustained in the past year, as well as potential risk factors: training volume and antecedent sport participation. Group comparisons were conducted. RESULTS: 82 athletes participated in the study. Respondents sustained over two injuries per year. Significant differences were found for sport category and type of injury. No differences were observed regarding antecedent sport participation. DISCUSSION: High prevalence and sport-specific injuries observed in university sport should be of concern to athletes, therapists, coaches and sport organizers. CONCLUSION: This study contributed to a better knowledge of injury patterns among university athletes, and suggests further practical and research implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".