Secondary and Tertiary Prevention for Adolescent Dating Violence: A Systematic Review
Bibliographic record
Abstract
Adolescent dating violence (ADV) is a pervasive public health issue associated with numerous social, psychological, and physical health consequences. Thus, programs are often implemented to prevent ADV and promote healthy relationships. Although there is a growing body of literature on primary ADV prevention strategies (i.e., prevention), little is known about secondary (e.g., early intervention) and tertiary (e.g., manage and reduce impact once occurring) ADV prevention approaches. This systematic review, guided by Cochrane Review methodology, summarizes available evidence on secondary and tertiary ADV preventive interventions. The search had no date restriction and was conducted in eight databases in November 2022. Studies published in English and/or Spanish were included if they described the development, implementation, and/or evaluation of a secondary and/or tertiary preventive intervention for ADV. After screening the titles and abstracts of 3,645 articles, 31 articles were included in this study, reporting on 14 secondary, 3 primary/secondary, 6 secondary/tertiary, and 1 tertiary ADV preventive intervention. The included studies highlighted that available secondary ADV prevention strategies are quite effective in preventing ADV victimization and perpetration, and that the effects may be strongest for teens with a higher risk of being involved in an abusive relationship. The only included study that reported on a tertiary intervention was a program development study. Based on the lack of tertiary prevention strategies available for ADV, clinical interventions focusing on treating and reducing negative consequences after ADV are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".