Implementing an Organizational Trauma-Informed Approach to Olympic Sites: An Urgent Priority to Protect Elite Athlete Well-being During Olympic Games Participation
Bibliographic record
Abstract
Abuse has been acknowledged as an adverse event which leads to trauma and long-term health effects in sport. Given the high rates of abuse occurring in elite sport contexts, many Olympic athletes will not only be subjected to abuse while residing and competing at the Olympic Games but may also experience trauma and its effects. In this article, we build on the calls for a trauma-informed approach in elite sport to outline a rationale for the International Olympic Committee (IOC) to implement an organizational trauma-informed approach to Olympic sites. Such an approach is essential because trauma researchers outside, and inside sport contexts, have outlined that when organizations are not trauma aware, and practices are not trauma-informed, unintended “unsafe” responses may result. To contextualize our rationale for an organizational trauma-informed approach, we provide examples of Olympic athletes’ stories to demonstrate the abuse and trauma they experienced while competing at the Olympic Games. To build on the human right that all athletes participating in the Olympic Games have the right to do so safely and free from harm, we further outline what an organizational trauma-informed approach involves and why it is important to limit re-traumatization risk. We further reflect on how being trauma-informed extends a duty of care to better protect athletes, which should be a responsibility of the IOC.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".