IOC consensus statement: interpersonal violence and safeguarding in sport
Bibliographic record
Abstract
OBJECTIVE: Interpersonal violence (IV) in sport is challenging to define, prevent and remedy due to its subjectivity and complexity. The 2024 International Olympic Committee Consensus on Interpersonal Violence and Safeguarding aimed to synthesise evidence on IV and safeguarding in sport, introduce a new conceptual model of IV in sport and offer more accessible safeguarding guidance to all within the sports ecosystem by merging evidence with insights from Olympic athletes. METHODS: A 15-member expert panel performed a scoping review following Joanna Briggs Institute methodologies. A seminal works-driven approach was used to identify relevant grey literature. Four writing groups were established focusing on: definitions/epidemiology, individual/interpersonal determinants, contextual determinants and solutions. Writing groups developed referenced scientific summaries related to their respective topics, which were discussed by all members at the consensus meeting. Recommendations were then developed by each group, presented as voting statements and circulated for confidential voting following a Delphi protocol with ≥80% agreement defined a priori as reaching consensus. RESULTS: Of 48 voting statements, 21 reached consensus during first-round voting. Second-round and third-round voting saw 22 statements reach consensus, 5 statements get discontinued and 2 statements receive minority dissension after failing to reach agreement. A total of 43 statements reached consensus, presented as overarching (n=5) and topical (n=33) consensus recommendations, and actionable consensus guidelines (n=5). CONCLUSION: This evidence review and consensus process elucidated the characterisation and complexity of IV and safeguarding in sport and demonstrates that a whole-of-system approach is needed to fully comprehend and prevent IV. Sport settings that emphasise mutual care, are athlete centred, promote healthy relationships, embed trauma- and violence-informed care principles, integrate diverse perspectives and measure IV prevention and response effectiveness will exemplify safe sport. A shared responsibility between all within the sports ecosystem is required to advance effective safeguarding through future research, policy and practice.
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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 | 0.132 | 0.254 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".