Mental distress is associated with injury and illness in elite Para athletes: a 44-week prospective study over 13 860 athlete days
Bibliographic record
Abstract
Background: The interest in elite athletes' mental health has increased over the past decade. However, there is still a paucity in the literature concerning elite Para athletes' mental health and its association with injuries and illnesses affecting athletes' participation in sports. Objective: To assess the weekly prevalence of mental distress and its association with injury, illness, sleep duration, hours of participation in sports and perceived exertion over a 44-week period in a cohort of Swedish elite Para athletes. A secondary aim was to describe the period prevalence of mental distress. Methods: This was a prospective longitudinal study including 59 Swedish elite Para athletes with physical, visual and intellectual impairments. Each week athletes reported mental distress according to 'The four-item Patient Health Questionnaire for Anxiety and Depression' (PHQ-4), sleep duration, hours of sports participation, perceived exertion as well as any new injury or illness. Descriptive statistics and multilevel regression analyses were used to analyse data. Results: The weekly prevalence was 15% for symptoms of anxiety, and 21% for depressive symptoms. The 44-week period prevalence was 58% for anxiety, and 42% for depressive symptoms. Multilevel logistic regression analyses revealed significant associations between reporting symptoms of mental distress with an ongoing injury, illness, and low sleep duration. Conclusion: Elite Para athletes report a moderate rate of symptoms of anxiety and depression, and there were associations between reporting mental distress and experiencing an injury or illness affecting athletes' participation in sports. The findings highlight the importance of longitudinal mental health monitoring and multidisciplinary support systems targeting those athletes reporting mental distress, injury or illness.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".