Verbal Versus Physical Victimization From Other People's Drinking: How Do Gender, Age, and Their Interactions With Drinking Pattern Affect Vulnerability?
Bibliographic record
Abstract
OBJECTIVE: The main objectives of this study were to determine (1) the extent of differential association of gender and age with being the victim of aggression by someone who has been drinking (i.e., alcohol-related victimization) for both verbal and physical forms of victimization and (2) whether drinking status/pattern interacts with gender and age in predicting verbal and physical victimization. METHOD: A general population survey of Canadian adults ages 18-76 was conducted using random digit dialing and computer-assisted telephone interviewing. Respondents who reported verbal victimization only, physical victimization only, and any combination of verbal and physical victimization were compared with those who reported no victimization. RESULTS: Verbal victimization was significantly more likely for women than for men, whereas physical victimization was more likely for men than for women. Younger age was more strongly associated with physical than with verbal victimization. In terms of significant interaction effects, the relationship between heavy episodic drinking (HED) and experiencing verbal victimization alone and combined verbal and physical victimization (but not for physical victimization alone) was significant for women but not for men. HED was significantly associated with experiencing combined verbal and physical victimization for younger people but not for older people. CONCLUSIONS: Future research on alcohol-related victimization needs to take into consideration the nature of alcohol-related victimization (e.g., verbal vs physical) and potential interactions involving gender and age. The significant relationship between HED and combined verbal and physical victimization for younger persons suggests that prevention efforts aimed at decreasing heavy drinking among young people may reduce their risk of victimization.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".