A Commentary on Mental Health Research in Elite Sport
Bibliographic record
Abstract
Elite athletes may be as likely as members of the general population to experience mental disorders, and there has recently been a surge of research examining mental health among athletes. This paper provides an overview and commentary of the literature on the mental health of elite athletes and explores how trends within and beyond the field of sport psychology have impacted this literature. Reviewing the contextual influences on this field, namely disorder prevalence, barriers to support seeking, mental toughness, and psychiatric epidemiology, are important to understand the broader picture of mental health research and to further strengthen work undertaken in sport psychology. In addition, appreciating the influence of various contextual factors on athlete mental health research can help to highlight where sport psychology practitioners may focus their attention in order to advance research and applied practice with elite athletes experiencing poor mental health. It is important that researchers consider how they measure mental health, how studies on the mental health of elite athletes are designed, implemented, and evaluated, and how both researchers and practitioners may help to combat athletes’ perceptions of stigma surrounding mental health. Considering topics such as these may lead to a deeper understanding of athlete mental health, which may in turn help to inform sport specific policies, applied practice guidelines, and interventions designed to enhance athlete mental health. Lay Summary: Recently, there has been an expansion of research on the mental health of elite athletes. We discuss some factors that have influenced the study of elite athlete mental health and how these factors continue to shape the field. We propose ways that researchers and practitioners may advance work in this area.
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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 | 0.023 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.063 | 0.052 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".