Association of self-reported health problems and interpersonal violence in sport: a cross-sectional study in world-level performing athletes
Bibliographic record
Abstract
OBJECTIVES: A paucity of studies assesses the intersection of physical health (injury and illness), mental health and experiences of interpersonal violence (IV, also known as harassment and abuse) in sport. The objectives of this study were to examine the (a) frequency of self-reported physical and mental health problems of elite athletes in the 12 months prior to the survey, (b) differences in physical and mental health between male and female athletes and (c) relationship of athlete health with experiences of IV. METHODS: Elite adult athletes from four sports were approached at eight international events to answer an online questionnaire on their physical and mental health, as well as experiences of IV in sport within the past 12 months. RESULTS: A total of 562 athletes completed the questionnaire. Overall, 75% reported at least one physical symptom, most commonly headache and fatigue (n=188; 33.5% each), followed by musculoskeletal symptoms (n=169; 29.4%). 65.1% reported at least one mental health symptom, mostly of anxiety or depression. More female than male athletes reported physical (F:81.9%; M:68.3%; p<0.001) and mental (F:71.9%; M:58.4%; p<0.001) health problems, while addiction problems were more frequent in male athletes (F:1.8%; M:6.4%; p=0.006. 53.0% of the female and 42.3% of the male participants reported having experienced at least one form of IV. Linear regression analysis demonstrated that all forms of IV, except physical IV (all p's<0.001), were associated with an increasing number of physical and mental health symptoms. In addition, the analysis showed that female athletes had a higher increase in symptoms in response to IV than male athletes. CONCLUSIONS: This study demonstrates the relationship of elite athlete physical and mental health with IV. Injury and illness prevention programmes in international sport should include strategies to reduce IV.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".