Research on Law and Policy to Prevent Teen Dating Violence: Scoping Review
Bibliographic record
Abstract
Teen dating violence (TDV) is a global public health issue with numerous consequences for physical, psychological, social, and emotional well-being. Thus, prevention of TDV has been a focus of research attention for the past several decades. As part of a comprehensive TDV prevention approach, programs, practices, and policies are needed. Yet, no prior research has reviewed the state of the science on laws and policies designed to prevent or address TDV. Thus, the objective of this scoping review was to identify existing global, empirical research on law and policy for TDV prevention and intervention at the municipal, provincial/state/territorial, or federal/national levels. Through comprehensive searches in eight databases in February 2022 and January 2024, we located 4,826 articles for potential inclusion. From this pool, articles were included if they focused on adolescents and on TDV-relevant law or policy at the local/municipal/school, state/provincial/territorial, or federal/national level(s), and were published in a peer-reviewed journal in English between January 1983 and December 2023. Following title/abstract screening and full-text review, 19 studies were ultimately included. These 19 studies focused on TDV law and policy content (36.8%, n = 7), implementation (36.8%, n = 7), and outcomes (26.3%, n = 5). All studies but one were conducted in high-income countries. Findings from this body of work may be useful as other jurisdictions develop TDV prevention and intervention laws and policies. Future work is also needed to understand the developmental, contextual, and policy context for TDV prevention outside of high-income, Western countries.
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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 | 0.025 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".