Prevalence and comorbidity of psychiatric disorders among treatment-seeking elite athletes and high-performance coaches
Bibliographic record
Abstract
Objectives: Few studies have evaluated the prevalence of psychiatric disorders among treatment-seeking elite athletes (EA) or high-performance coaches (HPC) in psychiatric outpatient settings. Methods: Descriptive overview of EA and HPC with psychiatric disorders at two publicly funded psychiatric outpatient treatment clinics in Stockholm and Malmö, Sweden. Co-occurring psychiatric disorders were illustrated using Venn diagrams for EA and HPC, and male and female EA separately, among patients from the Stockholm clinic (SC) that used standardised diagnostic interviews. Results: Overall, most patients were EA (n=221) compared with HPC (n=34). The mean age was 23.5 (±5.9) years for EA and 42.8 (±8.8) for HPC. Anxiety disorders were most common at the SC in EA and HPC (69% vs 91%, respectively). Stress-related disorders were found in 72% of HPC compared with 25% of EA. Affective disorders were found in 51% of EA and 52% of HPC. Eating disorders were common among EA (26%), especially females (37%). Substance use disorders were found in 17% of HPC. Comorbidity was generally common between affective and anxiety disorders. Conclusion: Stress and adjustment disorders were found in nearly three of the four HPC compared with one in four EA. Eating disorders were prevalent in around one in four athletes and about one in six HPC had a substance use disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".