Alcohol-related family violence in Australia: Secondary data analysis of the National Drug Strategy Household Survey
Bibliographic record
Abstract
Aims: Alcohol is a risk factor for family violence that affects partners, parents, children and other relatives. This study aims to provide estimates of the prevalence of alcohol-related family violence reported in 2016 in Australia across numerous socio-demographic groups.
 Methods: This paper presents secondary data analysis of 23,749 respondents (10,840 men, 12,909 women) from the Australian Institute of Health and Welfare’s 2016 National Drug Strategy Household Survey (NDSHS). Alcohol-related family violence was measured by self-report as being physically or verbally abused or put in fear from a family member or partner deemed by the victim as under the influence of alcohol. Logistic regression was used to analyse which factors were associated with alcohol-related family violence. 
 Findings: Analysis revealed that 5.9% of respondents (7.7% of women and 4.0% of men) reported alcohol-related family violence in the past year from either a partner or another family member. Respondents who were women (vs men), within less advantaged (vs more advantaged) socio-economic groups, risky drinkers (vs non-risky drinkers), residing in outer regional areas (vs major cities), holding a diploma (vs high school education) and single with dependents, reported higher overall rates of alcohol-related family violence. In contrast, respondents aged 55+ had significantly lower odds of experiencing alcohol-related family violence than all other age groups.
 Conclusions: Alcohol-related family violence was significantly more prevalent amongst respondents in a range of socio-demographic categories. Identification of these groups which are adversely affected by the drinking of family and partners can aid in informing current policy to protect those more vulnerable.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".