Bystander-Based Sexual Violence Prevention With College Athletes: A Pilot Randomized Trial
Bibliographic record
Abstract
The prevention of sexual violence on college campuses is a pressing public health issue. Given recent U.S. federal and state requirements for campus responses to sexual assault, many campuses may plan to implement brief, bystander-based programs to create a violence-free environment. This pilot study evaluates one such program for male undergraduate athletes, Wingman 101. The primary purpose of this study was to determine the feasibility and acceptability of participation in Wingman 101, as well as barriers to program implementation. Data for this project were collected from 80 undergraduate male athletes ( M age = 19.99) on three contact sport teams in spring 2012. Participants were randomly assigned to program or a no-program control condition. Implementation data were collected at the end of each session from program participants and facilitators. Outcome data were collected over three waves (pretest, posttest, 2-month follow-up) and assessed bystander attitudes, beliefs, and behaviors. Analysis of implementation data indicated that the program was well received and implemented with high fidelity, with facilitator relatability emerging as a particularly important aspect of implementation. However, participants also listed numerous barriers to potential bystander intervention following the program. The presence of these barriers supports quantitative reductions in positive attitudes about intervention at posttest. Implications of findings for policy and practice on postsecondary campuses are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".