International Olympic Committee consensus-driven guidelines for athlete mental health support at sporting events
Bibliographic record
Abstract
Event-related mental health problems among elite athletes are prevalent. However, empirical details on the development and implementation of a comprehensive mental health support programme at international sporting events are lacking. Therefore, this paper aims to provide consensus-driven, evidence-based recommendations to guide such a development and implementation. An 8-stage process based on the RAND-UCLA Appropriateness Method was adopted to collate and synthesise the available literature on this topic, to identify gaps in the scientific evidence and to enlist expert experience from a multidisciplinary expert panel to fill these gaps. Within such a process, the existing scientific literature was explored while experts (including athletes) were consulted to reach consensus on the recommendations. Accordingly, a robust pre-event plan is required to ensure the successful implementation of a comprehensive mental health programme during sport events, focussing on promotion, prevention, treatment and recovery. During sporting events, according to the defined pre-event plan, various activities targeting athletes, coaches, the entourage, officials or fans should be implemented: for example, educational programmes to decrease stigma, raise awareness and support help-seeking, a mental fitness area for decompression and relaxation, inperson mental health services provided by qualified mental health professionals with sport-specific as well as trauma-informed and violence-informed skills and mental health surveillance in parallel with the existing injury and illness surveillance programmes. Post event, a thorough evaluation of the implemented activities should be conducted while surveillance data should be analysed to identify areas requiring future targeted intervention(s). Also, particular attention should be given to postevent mental health support including the use of decompression interventions to support adjustment, emotional processing and reintegration.
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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 | 0.226 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.017 | 0.012 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".