The role, readiness to change and training needs of the Athlete Health and Performance team members to safeguard athletes from interpersonal violence in sport: a mini review
Bibliographic record
Abstract
Safeguarding athletes from interpersonal violence (IV) in sport is an important topic of concern. Athlete Health and Performance (AHP) team members working with athletes have a professional, ethical, and moral duty to protect the health of athletes, prevent IV, and intervene when it occurs. However, little is known on their respective roles regarding IV in sport and their needs to fulfill their responsibility of safeguarding athletes. The aim of this review is to synthesize knowledge about the roles, readiness to change and training needs of AHP team members to navigate IV in sport. A total of 43 articles are included in the review. Results show that all AHP team members have a role to play in safeguarding athletes and should therefore be trained in the area of IV in sport. Overall, very little research has directly assessed AHP team members' needs to positively foster safety and eliminate IV in sport. There are common training needs for all types of AHP team members such as the ability to recognize signs and symptoms of IV in sport. However, there are also specific needs based on the role of the AHP team members such as ways of facilitating behavioural change for sport managers. Findings from this review are mostly experts' recommendations and should therefore be interpreted as such. The results of the review can guide the development of future research and recommendations.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".