Mental health management of elite athletes during COVID-19: a narrative review and recommendations
Bibliographic record
Abstract
Elite athletes suffer many mental health symptoms and disorders at rates equivalent to or exceeding those of the general population. COVID-19 has created new strains on elite athletes, thus potentially increasing their vulnerability to mental health symptoms. This manuscript serves as a narrative review of the impact of the pandemic on management of those symptoms in elite athletes and ensuing recommendations to guide that management. It specifically addresses psychotherapy, pharmacotherapy and higher levels of care. Within the realm of psychotherapy, crisis counselling might be indicated. Individual, couple/family and group psychotherapy modalities all may be helpful during the pandemic, with novel content and means of delivery. Regarding pharmacotherapy for mental health symptoms and disorders, some important aspects of management have changed during the pandemic, particularly for certain classes of medication including stimulants, medications for bipolar and psychotic disorders, antidepressants and medications for substance use disorders. Providers must consider when in-person management (eg, for physical examination, laboratory testing) or higher levels of care (eg, for crisis stabilisation) is necessary, despite potential risk of viral exposure during the pandemic. Management ultimately should continue to follow general principles of quality health care with some flexibility. Finally, the current pandemic provides an important opportunity for research on new methods of providing mental health care for athletes, and consideration for whether these new methods should extend beyond the pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".