Persistently high impact of alcohol use on fatal violence in Lithuania despite strengthening alcohol control policies, 2004–19
Bibliographic record
Abstract
A strong association between alcohol and violence and homicide has been well established. Much less is known about the relationship between alcohol policies and the perpetration of alcohol-involved homicides, especially in the Central and Eastern European region. Despite recent progress, Lithuania still has one of the highest alcohol per capita consumption and homicide rates in the European region. Using quarterly data on homicide perpetrators in Lithuania for 2004-19, interrupted time-series were performed to evaluate whether the 2017 and 2018 alcohol control policies had an impact on the rate of perpetrators of homicide and the proportion of perpetrators under the influence of alcohol using a generalized additive model and generalized linear model, respectively. Although a rapid decline was observed in both the absolute numbers of homicides and rates of homicide perpetrators between 2004 and 2019, the proportion of homicide perpetrators under the influence of alcohol remained high. The analyses revealed that there was no significant effect of either of the two alcohol control policies on the rate of homicide perpetrators or the proportion of perpetrators under the influence of alcohol. The problem of persistently high occurrence of alcohol-involvement in homicides cannot be addressed by implementing alcohol control policies alone and thus, requires more inter-sectorial policy actions. More research is needed to understand homicide contexts and factors from both the victim and perpetrator perspectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".