A Meta-Analysis of Risk and Protective Factors for Dating Violence Victimization: The Role of Family and Peer Interpersonal Context
Bibliographic record
Abstract
Dating violence (DV) is a widespread social issue that has numerous deleterious repercussions on youths' health. Family and peer risk factors for DV have been widely studied, but with inconsistent methodologies, which complicates global comprehension of the phenomenon. Protective factors, although understudied, constitutes a promising line of research for prevention. To date, there is no comprehensive quantitative review attempting to summarize knowledge on both family and peer factors that increase or decrease the risk for adolescents and emerging adults DV victimization. The current meta-analysis draws on 87 studies with a total sample of 278,712 adolescents and young adults to examine effect sizes of the association between various family and peer correlates of DV victimization. Results suggest small, significant effect sizes for all the family (various forms of child maltreatment, parental support, and parental monitoring) and peer factors (peer victimization, sexual harassment, affiliation with deviant peers, and supportive/prosocial peers) in the prediction of DV. With few exceptions, forms of DV (psychological, physical, and sexual), gender, and age did not moderate the strength of these associations. In addition, no difference was found between the magnitude of family and peer factors' effect sizes, suggesting that these determinants are equally important in predicting DV. The current results provide future directions for examining relations between risk and protective factors for DV and indicate that both peers and family should be part of the development of efficient prevention options.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".