Surveillance of athlete mental health symptoms and disorders: a supplement to the International Olympic Committee’s consensus statement on injury and illness surveillance
Bibliographic record
Abstract
In 2019, the International Olympic Committee (IOC) published a consensus statement outlining the principles for recording and reporting injury and illness in elite sport. The authors encouraged sport federations to adapt the framework to their sport-specific context. Since this publication, several sports have published extensions to the IOC consensus statement.In response to a paucity of epidemiological data on athlete mental health, the IOC mental health working group adapted the IOC consensus statement on injury and illness surveillance to improve the capturing of athlete mental health data. In addition to the members of the working group, other experts and athlete representatives joined the project team to address gaps in expertise, and to add stakeholder perspective, respectively. Following an in-person meeting, the authors worked remotely, applying the scientific literature on athlete mental health to the IOC injury and illness surveillance framework. A virtual meeting was held to reach consensus on final recommendations.Practical outcomes based on the analysis of the scientific literature are provided with respect to surveillance design, data collection and storage, data analysis and reporting of athlete mental health data. Mental health-specific report forms for athlete and health professional utilisation are included for both longitudinal and event-specific surveillance.Ultimately, this publication should encourage the standardisation of surveillance methodology for mental health symptoms and disorders among athletes, which will improve consistency in study designs, thus facilitating the pooling of data and comparison across studies. The goal is to encourage systematic surveillance of athlete mental health.
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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 | 0.126 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".