Cumulative Violence Exposure and Alcohol Use Among College Students: Adverse Childhood Experiences and Dating Violence
Bibliographic record
Abstract
Multiple types of childhood adversities are risk factors for dating violence among college-age youth and in turn, dating violence is associated with alcohol use. This work quantitatively examines associations of childhood adversity and dating violence with alcohol use among college students using a cumulative stress approach. Multi-campus surveys were collected from March to December 2016 in four universities across the United States and Canada ( n = 3,710). Latent class analysis identified patterns of childhood adversity and dating violence. Regression analyses investigated the associations of latent class patterns with past year number of drinks, alcohol use frequency, and problematic drinking. Latent class analysis produced seven classes: “low violence exposure” (18.5%), “predominantly peer violence” (28.9%), “peer violence and psychological child abuse” (10.8%), “peer and parental domestic violence” (9.9%), “peer and psychological dating violence” (17%), “peer and dating violence” (6.6%), and “childhood adversity and psychological dating violence” (8.3%). Compared to the “low violence exposure” group, “peer and psychological dating violence” ( B = .114, p < .05), “peer and dating violence” ( B = .143, p < .05), and “childhood adversity and psychological dating violence” ( B = .183, p < .001) groups were significantly associated with problematic drinking. Results highlight how childhood adversity and dating violence contribute to problematic alcohol use, suggesting interventions that address both childhood adversity and dating violence may be most effective at reducing alcohol misuse among college students.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".