Alcohol’s Role in Campus Sexual Assault Bystander Behavior: A Systematic Review
Bibliographic record
Abstract
Between 50% and 70% of campus sexual assaults (SA) involve alcohol and campuses frequently promote bystander intervention strategies to prevent SA in student party contexts. This systematic review evaluates the measurement and outcomes of quantitative studies on how alcohol affects campus SA bystander outcomes. Using four search strategies and rigorous systematic review methods, we conducted a review of 36 studies. We included studies published after 2,000 conducted with college students in the United States, its territories, and Canada which contained information on alcohol use and SA bystander outcomes (behaviors and upstream proxies). Only a third of included studies measured bystander behavior directly, the majority measured upstream proxies (e.g., intentions, self-efficacy). Most studies considered the influence of victim intoxication on bystander outcomes. No studies assessed the pharmacological or physiological effects of alcohol on bystander outcomes. A single study examined hypothetical bystander responses when intoxicated, one-third examined bystander's typical alcohol use in relation to bystander outcomes. Many findings are contradictory, including fundamental details such as how often students encounter SA involving alcohol or whether bystanders' alcohol use predicts outcomes. Findings suggest that students are less likely help intoxicated victims compared to sober victims, but there are clear moderating factors. Perpetrator intoxication was not studied in relation to bystander behavior and did not influence any upstream proxies. Evaluations of interventions on alcohol and bystander topics are promising yet more research is required. This review illuminates key gaps in the literature, including the need for validated measures and scenarios, event-level studies, and alcohol administration trials.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| 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) | 0.005 | 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".