Athlete mental health: future directions
Bibliographic record
Abstract
The impact of mental health symptoms and disorders in elite athletes is increasingly recognised. This led the International Olympic Committee (IOC) to produce a consensus statement1 and establish a Mental Health Working Group. Members of this group have extensive experience in research and practice in the field of athlete mental health and have collectively ascertained gaps in current knowledge and practices. This editorial reflects the authors’ opinions and aims to provide researchers and practitioners with future directions relating to mental health symptoms and disorders in elite sport, focusing on prevalence/incidence, prevention, screening, assessment and treatment. The prevalence studies that are currently available have significant limitations,2 including the lack of data distinguishing mental health symptoms from disorders . The latter require a clinical assessment accounting for the intense and unique demands athletes face, which influence how symptoms and disorders may manifest. An example is the presence of mental health symptoms in overtrained athletes, how these are understood from perspectives such as low energy availability or impaired immune function and where existing classification systems may be inadequate. Studies are limited by the influence of stigma related to mental health symptoms and disorders both in and out of sporting contexts, which likely impact athletes’ responses and do not take account of additional barriers to reporting, specifically cultural barriers including sex, religion, …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".