A Systematic Review of Bystander Interventions for the Prevention of Sexual Violence
Bibliographic record
Abstract
INTRODUCTION: Bystander interventions have been successful in changing bystander attitudes and behaviors to prevent sexual violence. This systematic review was performed to summarize and categorize the characteristics of sexual violence bystander intervention programs and analyze bystander intervention training approaches for the primary prevention of sexual violence and assault. METHOD: From June to July 2017, the authors searched both published and unpublished American and Canadian studies from 2007 to 2017. The published sources included six major electronic databases and the unpublished sources were Google Scholar and the 40 program websites. From the 706 studies that resulted from this initial search, a total of 44 studies (that included a single bystander intervention program and assessments at both pretest and at least one posttest) were included. RESULTS: Thirty-two percent of studies analyzed bystander behavior postintervention, and most found significant beneficial outcomes. The most frequently used training methods were presentation, discussion, and active learning exercises. Bringing in the Bystander and The Men's Program had the most replicated empirical support for effectiveness. DISCUSSION: There has been a substantive increase in quasi-experimental and randomized controlled trial approaches to assessing the effectiveness of this type of intervention since 2014. The training methods shared between these efficacious programs may translate to bystander interventions for other victimization types, such as child abuse. CONCLUSION: The use of in-person bystander training can make positive changes in attitudes and behaviors by increasing awareness of a problem and responsibility to solve it.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".