The Prevalence of Failure-Based Depression Among Elite Athletes
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of diagnosed failure-based depression and self-reported symptoms of depression within a sample of elite swimmers competing for positions on Canadian Olympic and World Championship teams. DESIGN: A cross-sectional design. SETTING: Assessments were conducted after the conclusion of the qualifying swimming trials. PARTICIPANTS: The sample consisted of 50 varsity swimmers (28 men and 22 women) based at 2 Canadian universities who were competing to represent Canada internationally. MAIN OUTCOME MEASURES: Diagnosed depression was assessed using a semistructured interview, and symptoms of depression were also assessed by the Beck Depression Inventory II. Performance was measured by changes in swimming time and athlete ranking. RESULTS: Before competition, 68% of athletes met criteria for a major depressive episode. More female athletes experienced depression than their male peers (P = 0.01). After the competition, 34% of athletes met diagnostic criteria and 26% self-reported mild to moderate symptoms of depression. The prevalence of depression doubled among the elite top 25% of athletes assessed. Within this group, performance failure was significantly associated with depression. CONCLUSIONS: The findings suggest that the prevalence of depression among elite athletes is higher than what has been previously reported in the literature. Being ranked among the very elite athletes is related to an increase in susceptibility to depression, particularly in relation to a failed performance. Given these findings, it is important to consider the mental health of athletes and have appropriate support services in place.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".