Physical Violence Perpetration Among College Students: Prevalence and Associations With Substance Use and Mental Health Symptoms
Bibliographic record
Abstract
The aims of this study were to, first, report the prevalence of physical violence perpetration among a sample of college students and, second, to identify associations between physical violence perpetration, substance use, and mental health symptoms. We analyzed survey data from the Healthy Minds Study. We examined the 12-month prevalence of physical violence perpetration by gender identity from 2014–2019 ( n = 181,056). We used multivariable logistic regression analyses to estimate associations between physical violence perpetration, substance use, and mental health symptoms from the 2018–2019 survey year ( n = 43,563). Results revealed that 12-month prevalence rates of physical violence perpetration increased from 2014–2019 among male, female, and transgender/gender nonconforming college students. Results from multivariable logistic regression analyses using the 2018–2019 survey year revealed higher odds of physical violence perpetration in the previous 12 months among students who reported substance use and mental health symptoms, including vaping or e-cigarette use, illicit drug use, and nonsuicidal self-injury, among others. Our findings highlight steadily rising prevalence of physical violence perpetration from 2014–2019 among college students, indicating a growing need for more research and prevention efforts to address this problem in higher education settings. Efforts to prevent violence on college campuses should consider how to reduce substance use and improve mental health to reduce this form of violence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".