The impact of alcohol control policy on assaults and sexual assaults in Lithuania: An interrupted time-series analysis
Bibliographic record
Abstract
OBJECTIVES: The aim of the current study was to test the impact of three alcohol control policy enactments (in 2008, 2017 and 2018) on assaults and sexual assaults in Lithuania. The hypothesis tested was that alcohol control policy implementation is associated with a reduction in the occurrence of both assaults and sexual assaults. STUDY DESIGN: An interrupted time-series analysis. METHODS: To estimate the unique impact of three alcohol control policies, interrupted time-series analyses were conducted. Three alcohol policy enactments, based on the World Health Organization "best buys" framework, and the following stringent criteria: (1) pricing policies had to have resulted in decreased affordability, defined in terms of the price of alcohol increasing at a higher degree than average disposable income; or (2) availability policies that aimed to reduce alcohol use for a large portion of the general population were selected for evaluation. RESULTS: The alcohol control policy implemented in 2017 was statistically significantly associated with a reduction of 29.9 % (exp(-0.35379)-1) in the rate of sexual assaults, after adjusting for the financial recession and COVID-19-related lockdowns and smooth functions of time. CONCLUSIONS: Study provides evidence that alcohol control policies, particularly those focusing on major alcohol tax increases that reduces alcohol affordability can contribute to reducing rates of sexual assault. The current findings, along with the consistent evidence linking alcohol use to sexual violence, supports the need for comprehensive strategies for mitigating violence to include alcohol.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".