A Review of Evidence-Based Dating Violence Prevention Programs With Behavioral Change Outcomes for Adolescents and Young Adults
Bibliographic record
Abstract
Adolescent dating violence (DV) is not only a social but also a public health problem, necessitating the development and scale-up of prevention strategies. We conducted a review of the literature to identify adolescent and young adult DV prevention programs that have shown promising behavioral outcomes. The literature search covered articles published from 1996 to 2022 and indexed in Medline, Cochrane, Scopus, PsycINFO, and Embase. The review focused on programs implemented and evaluated in the United States or Canada that included intervention and comparison groups, a baseline assessment, and at least one post-assessment conducted after the intervention exposure. Promising behavioral outcomes were defined as positive, statistically significant differences between intervention and comparison groups with respect to DV perpetration or victimization or bystander behavior in relation to DV. A total of 118 articles were screened by abstract and read in-depth. Eighteen programs that met the inclusion criteria were identified. Of these programs, one showed reductions in DV victimization, six showed reductions in DV perpetration, and nine showed behavioral reductions in both violence perpetration and victimization. The review highlighted that while multiple programs have demonstrated efficacy in preventing or reducing intimate partner violence in North American youth populations, more robust research on the replication of these programs outside researcher-controlled environments is needed. Furthermore, issues with program inclusivity, such as with sex and gender-minority individuals, should be considered in future intervention development and replication research.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".