What impact have the IOC medical consensus statements made on athlete health? A survey of medical commissions from National Olympic/Paralympic Committees and International Sports Federations
Bibliographic record
Abstract
Background: The International Olympic Committee (IOC) Medical and Scientific Commission has supported collating and sharing evidence globally by developing sports medicine consensus statements ('Statements''). Publishing the Statements requires substantial resources that must be balanced by use and impact on policy and practice. This study aimed to gain a better understanding of awareness and uptake of the Statements globally through a survey of the National Olympic Committees (NOC), National Paralympic Committees (NPC) and International Federations (IF). Method: A cross-sectional survey of medical commission representatives from NOCs/NPCs/IFs. A structured questionnaire was distributed through the IOC head office, informed by prior research. Questions comprised a mix of closed and open-text responses with results presented descriptively by organisation type and total. Results: 55 responses were included: 29 (52%) from NOC/NPC representatives (response rate 14%) and 26 (47%) from IF representatives (response rate 63%). All Statements had been used by at least one respondent, with the Statement addressing concussion ranked highest (used by 33/55). The main barriers to use were financial limitations (n=21), club/sport culture and behaviours (n=19) and lack of understanding from coaches/team sport personnel (n=19). Participants believed the Statements were a successful strategy for improving athlete health (n=39/51 agree or strongly agree). Conclusion: There was clear support for the continued development of sports medicine guidance, including in the format of these Statements. To ensure Statements lead to demonstrable health benefits for athletes, input from athletes, coaches and supporting staff is needed, as well as clearer identification of the purpose and audience of each topic developed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".