Neighborhood Social Support and Social Participation as Predictors of Dating Violence
Bibliographic record
Abstract
Many adolescents experience violence in the context of dating and romantic relationships. Neighborhoods can influence dating violence by offering certain resources which can provide social support and opportunities for social participation, but knowledge about these effects is still limited. The purpose of the current study was to (a) assess the association between neighborhood social support, social participation, and dating violence, and (b) explore possible gender difference in these associations. This study was conducted on a subsample of 511 participants living in Montréal from the Québec Health Survey of High School Students (QHSHSS 2016–2017). QHSHSS data were used to measure psychological and physical/sexual violence (perpetration and victimization), neighborhood social support, and social participation, as well as individual and family covariates. Several neighborhood-level data from multiple sources were also used as covariates. Logistic regressions were performed to estimate associations between neighborhood social support and social participation, and Dating violence (DV). Analyses were conducted separately for girls and boys to explore possible gender differences. Findings suggest that girls who reported high neighborhood social support had a lower risk of perpetrating psychological DV. High social participation was associated with a lower risk of perpetrating physical/sexual DV for girls, whereas it was associated with a higher risk of perpetrating psychological DV for boys. Preventive strategies to foster social support in neighborhoods, such as mentoring programs, and the development of community organizations to increase the social participation of adolescents could help reduce DV. To address the perpetration of DV by boys, prevention programs in community and sports organizations targeting male peer groups should also be developed to prevent these behaviors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".