Estimates and predictors of alcohol-related harm to intimate partners in Australia An analysis of a nationally representative survey
Bibliographic record
Abstract
Aims: This study explores the prevalence and predictors reported in 2019 by men and women of alcohol-related intimate partner violence (ARIPV), that is, verbal abuse, physical abuse, and being put in fear by intimate partners, when partners were under the influence of alcohol. Methods: Secondary data analysis of the 2019 Australia’s cross-sectional National Drug Strategy Household Survey included 22,015 respondents aged 14 years or older (9,804 men, and 12,211 women). The prevalence of ARIPV and each type of ARIPV (verbal abuse, physical abuse, and being put in fear) in the past year is described, and the predictors of all are analysed using chi-square tests and logistic regressions, overall and separately for men and women. Results: An estimated 3.4% of the Australian adult population (4.7% women, 2.1% men) reported ARIPV in 2019. The prevalence of ARIPV was higher among the participants who were women, middle-aged (35-44 years), had a certificate or diploma, were less advantaged, were divorced, separated, or widowed, single with dependents, living in more regional, and remote areas, and undertook heavy episodic drinking (HED) weekly or less often. Age, marital status, household composition, and weekly, monthly, or ever HED predicted ARIPV for women, while higher education levels and weekly or monthly HED were significant for men. Discussion and conclusions: Women were twice as likely to report intimate partner violence (IPV) from their male partner when they were under the influence of alcohol, as were men. The findings underline that interventions are needed to address IPV from intoxicated partners.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".