Disordered eating is not associated with musculoskeletal injury in university athletes
Bibliographic record
Abstract
Athletes have a greater risk of developing disordered eating (DE) behaviours than non-athletes. Literature suggests that DE is associated with injury in female athletes; however, these associations are understudied in both female and male athletes. Our objective was to examine the association between DE and injuries in varsity athletes. In this cross-sectional study, varsity student athletes attending a Canadian university completed an anonymous online survey. The survey included questions regarding demographics, injury occurrence, and the Disordered Eating Screen for Athletes (DESA-6; a score ≥3 is indicative of DE). Athletes were categorized by DE status and injury occurrence. Chi-square tests were performed to assess the relationship between these variables. Musculoskeletal injury frequency was compared between DE and non-DE groups using a Mann–Whitney test. Fifty-six varsity athletes ( N = 37 females, 66.1%) with a mean age of 20.1 ± 1.3 years participated in this study. DE was not associated with injury occurrence ( p = 0.73), and musculoskeletal injury frequency did not differ between DE and non-DE groups ( p = 0.50). However, both injury and DE were prevalent as 73.2% of participants reported injuries and 33.9% had positive DESA-6 scores. These findings highlight the need to address DE and injuries in athletes and could encourage the implementation of strategies to reduce their prevalence in sport. Take home message Musculoskeletal injuries and disordered eating are prevalent in varsity-level athletes but are not associated in our participants.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".