The Effectiveness of College Dating Violence Prevention Programs: A Meta-Analysis
Bibliographic record
Abstract
Due in part to their involvement with social activities on campus, college students experience an increased risk of dating violence. Recent legislation such as the Campus SaVE Act (which requires U.S. colleges to offer training on sexual assault, domestic violence, stalking, and sexual harassment to all incoming students) has contributed to the increase in prevention programming offered across postsecondary campuses, as well as subsequent research examining the effectiveness of these prevention efforts. The current study provides a systematic review and meta-analysis of college dating violence prevention programs. A systematic search of 28 databases and numerous gray literature sources identified an initial 14,540 articles of which 315 were deemed potentially eligible for inclusion. Studies were selected if they (1) evaluated a college dating prevention program/campaign, (2) reported one of five outcomes (knowledge, attitudes, or bystander efficacy, intentions, or behavior), (3) had a minimum sample size of 20 in the treatment group, (4) used a pre/post and/or comparison group design, and (5) were published in English or French between January 2000 and October 2020. We calculated 53 effect sizes from 31 studies and conducted separate meta-analyses on various categories of outcome measures. Findings suggest that college dating violence prevention programs are effective at increasing knowledge and attitudes toward dating violence, as well as bystander skills, but are not effective at increasing bystander behaviors. Findings from moderator analyses suggest that several program components influence the strength of treatment effects. Implications for improving the effectiveness of college dating violence prevention programs 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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".