SafeSport: Perceptions of Harassment and Abuse From Elite Youth Athletes at the Winter Youth Olympic Games, Lausanne 2020
Bibliographic record
Abstract
OBJECTIVE: To analyze the Winter Youth Olympic Games (YOG) 2020 athletes' understanding and perceptions of harassment and abuse in sport and their knowledge of reporting mechanisms. DESIGN: A cohort study. SETTING: The Winter YOG2020 in Lausanne, Switzerland. PARTICIPANTS: Accredited athletes at the YOG2020. INTERVENTION: An athlete safeguarding educational program was delivered at the YOG2020. Participating athletes were encouraged to answer a survey embedded in the safeguarding educational materials during the YOG. MAIN OUTCOME MEASURES: Perception of occurrence of harassment and abuse as well as knowledge of the term "safe sport" and reporting mechanisms. RESULTS: The survey response rate of athletes attending the Safe Sport Booth was 69%. When asked to define Safe Sport, 10% of athletes at the YOG2020 correctly identified a sport environment free from harassment and abuse, 20% identified fair play/antidoping, and 19% safety. When presented with the definition of harassment and abuse, 30.4% expressed surprise, in contrast to 46% in the summer YOG2018. A third (32%) reported that harassment and abuse was either "likely" or "very likely" present in their sport, which was similar to the YOG2018 (34%). The group of athletes not knowing where to go to report harassment and abuse was greater than in the YOG2018 (26% vs 11%). There were no differences in responses between competitive sex (boys' vs girls' events) or type of sport (team vs individual). CONCLUSIONS: Outcomes of this study, such as the development of youth-friendly terminology and emphasizing mechanisms for reporting of harassment and abuse, should inform the development of safeguarding educational materials for youth athletes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".