Mental health considerations for athlete removal from play and return to play planning
Bibliographic record
Abstract
Abstract: Introduction: Athletes experience the same mental health disorders as the general population. When mental health symptoms or disorders are experienced more acutely, there may be occasions when the treating team needs to decide if it is in the best interest of the athlete to be removed from the sport environment for treatment and recovery. If an athlete has been away from the sport environment due to mental health symptoms or disorders, the treating team should be deliberate and collaborative in guiding their return. Removal-from-play (RFP) and return-to-play (RTP) decisions involving an athlete who has experienced mental health challenges can be complex. Methods: The literature around athlete mental health was reviewed to explore contributing and mitigating factors to mental health challenges in this population. General psychiatric recovery trajectories for selected mental illnesses were reviewed to inform RTP planning through and beyond illness episodes. The literature related to RFP and RTP for athletes in terms of specific physical factors (concussion and musculoskeletal injury) and mental health factors (specifically, depression, anxiety, and eating disorders) was also reviewed. Results: A scoping overview of athlete- and sport-specific factors yielded a framework that can be used to guide athlete support, RFP and RTP planning through and beyond mental health-related sport interruption. Conclusion: When mental health symptoms and disorders are present, decisions guiding RFP and RTP should be guided by clinical assessment of safety, stability and function. Due to the complex nature of mental disorders and the interaction of sport elements, it is recommended that sports psychiatrists are involved in the assessment and management process.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".