#SafeSport: safeguarding initiatives at the Youth Olympic Games 2018
Bibliographic record
Abstract
BACKGROUND: Little is known about athletes' understanding of safe sport and occurrence of harassment and abuse in elite youth sport. OBJECTIVE: To evaluate the IOC Safe Sport educational experience at the Youth Olympic Games 2018 in Buenos Aires and to ascertain the athletes' (1) understanding of what constitutes harassment and abuse, (2) perception of the occurrence in their sport, and (3) knowledge of where to report. METHODS: Athletes visiting the IOC Safe Sport Booth answered a survey related to athletes' (1) understanding of harassment and abuse in sport, (2) perception of the occurrence of harassment and abuse in their sport, and (3) knowledge of where to report. Experts and volunteers answered an email survey on their experience. RESULTS: The response rate was 71.8%. When asked to define 'safe sport', the athletes mainly relate the concept to general physical and environmental safety, fair play and clean sport, rather than sport free from harassment and abuse. Almost half (46%) of the athletes expressed surprise by the definition of behaviours of harassment and abuse within sport. When asked if harassment and/or abuse occur in their sport, 47.5% reported 'no' or 'not likely', while 34% stated 'likely' or 'very likely'; 19% were 'unsure'. The majority (63%) of athletes knew where to seek help. Three quarters (71%) of the athletes rated the educational materials as 'good' to 'excellent'. The experts and volunteers believed the intervention would result in change in athletes' awareness, knowledge and behaviour. CONCLUSIONS: This multinational cohort of elite youth athletes is not knowledgeable of the concept of harassment and abuse in sport, despite there being a significant perception of occurrence of harassment and abuse in their sports.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".