Mental Health Among Elite Youth Athletes: A Narrative Overview to Advance Research and Practice
Bibliographic record
Abstract
CONTEXT: Participation in sports during youth is typically beneficial for mental health. However, it is unclear whether elite sport contexts contribute to greater risk of psychological distress or disorder. The aims of this paper are to highlight conceptual issues that require resolution in future research and practice, and to examine the key factors that may contribute to the mental health of elite youth athletes (EYAs). EVIDENCE ACQUISITION: A narrative overview of the literature combined with the clinical and research expertise of the authors. STUDY DESIGN: Narrative overview. LEVEL OF EVIDENCE: Level 5. RESULTS: EYAs experience a range of biopsychosocial developmental changes that interact with mental health in a multitude of ways. In addition, there are various sport-specific factors that contribute to the mental health of EYAs that may become more prominent in elite contexts. These include - but are not limited to - patterns relating to athlete coping and self-relating styles, the nature of peer, parental, and coach relationships, organizational culture and performance pressures, and mental health service provision and accessibility. CONCLUSION: A range of critical factors across individual, interpersonal, organizational, and societal domains have been shown to contribute to mental health among EYAs. However, this evidence is limited by heterogeneous samples and varied or imprecise terminology regarding what constitutes "youth" and "elite" in sport. Nevertheless, it is clear that EYAs face a range of risks that warrant careful consideration to progress to best practice principles and recommendations for mental health promotion and intervention in elite youth sport. SORT: Level C.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".