Childhood Community Risk Factors on Intimate Partner Violence Perpetration and Victimization Among College Students
Bibliographic record
Abstract
The study examined the effect of community environments, such as community cohesion, community safety, and community poverty, in childhood on the likelihood of Intimate Partner Violence (IPV) perpetration and victimization in young adulthood. The study used the cross-sectional survey data of 2,082 college students collected in 2016-2017 from six universities in the U.S. and the data for the childhood community environment from the 2007-2011 American Community Survey. Hierarchical regressions were performed separately by gender to 1) assess the effects of community factors in addition to individual factors for IPV perpetration and victimization, and to 2) identify the interaction effect of community cohesion with community poverty on IPV perpetration and victimization. Community factors of community cohesion and community poverty were significantly correlated to different types of IPV. For IPV perpetration, only community cohesion was significant for, the interaction effect between community cohesion and poverty showed that higher community cohesion lowered the risk of community poverty on later IPV perpetration in both genders. For IPV victimization, only female students were affected by community poverty, whereas none of the community factors had an impact on male students. The findings imply the significance of early interventions and policies strengthening the community environment, especially community cohesion, for preventing IPV. The findings also suggest that assessing risk and protective factors on IPV in multiple contexts during childhood is important to develop effective programs preventing IPV.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".