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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".