A Scoping Review of Bystander-Based Sexual Violence Prevention Training for College Students in Fraternities and Sororities
Bibliographic record
Abstract
Bystander-based sexual violence (SV) prevention trainings are offered on college campuses across the United States to meet federal Title IX requirements, as they have proven to be an effective strategy for violence prevention. Greek-affiliated students (fraternity and sorority members) are at a higher risk of sexual assault than their peers; however, few trainings consider the specific needs of this population, and program adaptations for this high-risk group may be needed. This scoping review identifies and describes the bystander trainings delivered to Greek-affiliated students in the US and Canada. An eight-database search was conducted following PRISMA-ScR guidelines. The review identified 81 unique sources, with 18 meeting the inclusion criteria. Eleven specific training programs were identified, encompassing qualitative, quantitative, and mixed-method studies. The thematic analysis revealed best practices, including the importance of peer leaders, interactive sessions, and tailored content to Greek culture, as well as barriers such as a lack of engagement and an inadequate session length. The review underscores the need for tailored interventions to effectively address the unique cultural characteristics and high-risk nature of Greek-affiliated students. These findings provide valuable insights for improving the design and implementation of bystander interventions to enhance their efficacy in preventing sexual violence within this population.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".