Alcohol Accessibility and Family Violence-related Ambulance Attendances
Bibliographic record
Abstract
There is substantial evidence supporting the association between alcohol license density and violent crime. However, the impact of different types of alcohol licenses on intimate partner and family violence is sparse. We explored the associations between access to alcohol outlets, and family and intimate partner violence using paramedic clinical records, given this service is often the first to respond to acute crises. Coded ambulance attendance data from 694 postcodes in Victoria, Australia, from July 1, 2016 to June 30, 2018 where alcohol or another drug, mental health or self-harm associated with family or intimate partner violence was indicated were examined. A hybrid model of spatial autoregressive and negative binomial zero-inflated Poisson-based count regression models was used to examine associations with alcohol outlet density and socioeconomic factors. We found that access to a liquor license outlet was significantly associated with family violence-related attendances across all types of outlets, including on-premise (late night) licenses ( β = 1.73, SE: 0.18), restaurant licenses ( β = 0.83, SE: 0.28), and packaged liquor licenses ( β = 0.62, SE: 0.06). Our results demonstrate a significant relationship between alcohol-related harms in the context of family violence and provides evidence of the relationship between alcohol-related family violence in both victims and perpetrators. The findings of this study highlight the need for public health interventions such as licensing policy and town planning changes to reduce these harms by restricting alcohol availability.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".