Evaluation of alcohol policy control measures is key
Bibliographic record
Abstract
In Lithuania, large income inequalities may also be linked to increasing health inequalities 1, 8, caused by an interaction between alcohol use, socio-economic status and rapid economic changes. To explore this further, the planned evaluation of alcohol control measures in this country 9 should include more analyses stratified by socio-economic strata. This could either be achieved at the individual level (e.g. using available national surveys and linking to national health databases) or at the population level, by using data on the economic wealth of different regions—readily available from national mortality and morbidity databases—for stratification purposes. There is an urgent need to further study the impact of Lithuania's natural experiment on alcohol control policy measures, as well as to inform national stakeholders of the results of these efforts. Further, by disseminating the findings of such studies in the international literature, other researchers may be inspired to conduct similar, much-needed research in this area. Over time, the public health emergency in Lithuania may serve as an exemplar for other small countries—countries that do not necessarily have the capacity to conduct such in-depth multi-dimensional studies themselves—of the effectiveness of the alcohol control policies adopted in Lithuania. Lastly, as Jasilionis pointed out, other external causes of mortality, such as deaths by suicide, have been declining at a much slower rate than alcohol-related traffic deaths 10. This could, in part, be due to the fact that individuals with an alcohol use disorder (AUD) are unlikely to recover as a result of population-level alcohol policies. Another explanation might be that such policies only have an effect on the prevalence of AUDs in the long term, and are therefore not immediately reflected in mortality statistics. Given that individuals with an AUD have a two- to threefold higher risk of dying by suicide compared to those without an AUD 11, this hypothesis could explain why other external causes of mortality are declining at a much slower rate. This is a line of research that should be explored, as lag times of risk factors on various disease and mortality outcomes are important in considering impacts, and to avoid raising unrealistic expectations. In conclusion, evaluations of alcohol control policy are key, and future policies need to be held against a standard where they are not only effective in reducing alcohol-attributable harm, but also in reducing health inequalities 12. J.B. and C. F.-B. are staff members of the WHO Regional Office for Europe. The authors alone are responsible for the views expressed in this publication and these do not necessarily represent the decisions or the stated policy of the World Health Organization. J.R. acknowledge funding from the Canadian Institutes of Health Research's Institute of Neurosciences, Mental Health and Addiction (Canadian Research Initiative on Substance Misuse Ontario Node GrantSMN-13950). The Institute of Neurosciences, Mental Health and Addiction is one of the Institutes of CIHR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".