Verbal Abuse as a Form of Intimate Partner Violence (IPV): Exploring Academic Performance, Mental Health, Loneliness, Relationship Quality, and Social Support Among Female and Male University Students
Bibliographic record
Abstract
Intimate partner violence (IPV) is multifaceted and concerning, particularly among postsecondary students. However, more subtle forms of IPV involving name calling, insults and/or belittling to undermine and/or manipulate one’s partner may be minimized, and underexplored in the literature. In a cross-sectional study, data extracted from the National College Health Assessment-III (NCHA-III) survey administered to students at a large Atlantic Canadian university ( N = 1,694; M age = 26.6 years) were used to explore whether academic, mental health, relationship, and social support outcomes statistically vary by verbal IPV. Overall, verbal IPV was reported by 11.3% of the entire student sample, with two-factor Chi-square tests indicating no significant prevalence differences in sex, citizenship, and undergraduate/graduate status. However, relative to students not reporting verbal IPV, two-factor Chi-square tests indicated that academic problems, poorer mental health, loneliness/isolation, family/peer relationship challenges, and bullying/cyberbullying were significantly more likely among students reporting verbal IPV, regardless of student sex. Subsequent independent t-tests revealed significantly lower ‘social integration’ and ‘reassurance of worth’ subdomain levels among those reporting verbal IPV. Moreover, separate hierarchical regressions featuring female and male students reporting verbal IPV revealed that university belongingness, and social isolation were significant predictors of psychological distress among females, while age, university belongingness, and ‘reassurance of worth’ were significant predictors of psychological distress among males. These findings underscore the substantial and specific impact of verbal IPV on students’ scholarly, psychological, and social/relationship functioning, and highlight the need for increased awareness, prevention, and trauma-informed support services within university settings. Implication of findings are considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".