Prevention of Sexual Violence in sport: A Socioecological Review
Bibliographic record
Abstract
Sexual violence in sport is prevalent and represents a serious public health concern. The social-ecological model for health promotion has been used successfully as a framework to identify individual-to-policy level factors aimed at health promotion or disease prevention. The purpose of this review was to examine both published and non-published (publicly available) SVP efforts conducted within the context of sport and make recommendations for future practice. Grey literature search methods were utilized to conduct a review of publicly available documents. This included (a) a comprehensive Google search using unique search terms that would identify SVP efforts within sport settings and (b) a review of the publicly accessible websites identified in the previous step. Following the grey literature search, and using the SVP practices identified in step one, we conducted a supplementary literature search using scientific publication search engines to identify whether the SVP practices identified in step one had associated peer-reviewed publications. Finally, we assessed various characteristics of each SVP practice including the target population, age range of intended participants, and whether the SVP had associated peer review publications. This led to the identification of 35 unique SVP practices: 25 (71%) SVP practices were assigned to the Intrapersonal level, 6 (17%) were assigned to the Interpersonal level, 9 (26%) were assigned to the Organizational level, 3 (9%) were assigned to the Community, and 2 (6%) were assigned to the Policy level. This review uncovered several important findings including a lack of multi-level SVP practices within sport, a lack of SVP practices that target children, minimal programming aimed at specifically preventing perpetration, the need to elevate policy level action, and a lack of peer-reviewed literature. Ultimately findings suggest that sport organizations ought to prioritize sexual violence prevention using national organizations for guidance.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".