Preventing Gender-Based Violence Among Adolescents and Young Adults: Lessons From 25 Years of Program Development and Evaluation
Bibliographic record
Abstract
Effective prevention of intimate partner violence (IPV) among adolescents and young adults is a key strategy for reducing rates of gender-based violence (GBV). Numerous initiatives have been developed and evaluated over the past 25 years. There is emerging evidence about effective strategies for universal prevention of dating violence in high school settings and effective bystander interventions on university and college campuses. In addition, there have been some effective practices identified for specific groups of youth who are vulnerable to victimization (either based on past experiences of exposure to domestic violence or previous dating victimization). At the same time, though our evidence about school and college-based interventions has grown, there are significant gaps in our knowledge of effective prevention among marginalized groups. For example, there is a lack of evidence-based strategies for preventing IPV among Indigenous youth; lesbian, gay, bisexual, transgender, questioning+ [LGBTQ+] youth; and young women with disabilities, even though these groups are at elevated risk for experiencing violence. Our review of the current state of evidence for effective GBV prevention among adolescents and young adults suggests significant gaps. Our analysis of these gaps highlights the need to think more broadly about what constitutes evidence. We identify some strategies and a call to action for moving the field forward and provide examples from our work with vulnerable youth in a variety of settings.
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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 | 0.054 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".