Mental health and well-being of elite youth athletes: a scoping review
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of the prevalence and risk factors for mental health symptoms and disorders among adult elite athletes, with less research involving elite youth athletes. This scoping review aimed to characterise the mental health and well-being of elite youth athletes who travel internationally and compete for their sport. METHOD: Four databases were searched in March 2023. Inclusion criteria were studies with elite youth athlete populations (mean age 12-17 years) reporting mental health and well-being outcomes. Data from included studies were charted by outcome, and risk/protective factors identified. RESULTS: Searches retrieved 3088 records, of which 33 studies met inclusion criteria, encapsulating data from 5826 athletes (2538 males, 3288 females). The most frequently studied issue was disordered eating (k=16), followed by anxiety (k=7), depression (k=5) and mixed anxiety/depression (k=2). Caseness estimates (a symptom level where mental health treatment is typically indicated) for disordered eating were wide ranging (0%-14% for males; 11%-41% for females), whereas only two studies estimated caseness for depression (7% in a mixed-sex sample; 14% for males, 40% for females) and one for anxiety (8% for males, 28% for females). Common risk factors for mental ill-health included sex, athlete status (compared with non-athletes) and social/relationship factors (with coaches/parents/peers). Contradictory evidence was observed for elite/competition level, which was associated with higher and lower rates of disordered eating. CONCLUSION: Further representative research into the mental health and well-being of elite youth athletes is needed to enhance understanding and guide prevention and intervention measures.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".