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Record W4210492688 · doi:10.5114/jhi.2021.113167

Alcohol and health in Central and Eastern European Union countries – status quo and alcohol policy options

2021· article· en· W4210492688 on OpenAlexaff
Jürgen Rehm, Mindaugas Štelemėkas, Kawon Victoria Kim, Anush Zafar, Shannon Lange

Bibliographic record

VenueJournal of Health Inequalities · 2021
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental HealthPublic Health OntarioUniversity of Toronto
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsStatus quoEuropean unionHarmConsumption (sociology)Alcohol consumptionBusinessEconomic policyEnvironmental healthEconomic growthAlcoholPolitical scienceEconomicsMedicineMarket economy

Abstract

fetched live from OpenAlex

The aim of this narrative review is to give an overview of alcohol consumption, attributable health harm, and potential alcohol control policies to reduce this harm in five Central and Eastern European Union countries: Czech Republic, Estonia, Latvia, Lithuania, and Poland. The overall level of alcohol consumption was high, with the two highest-consuming countries in the world being situated in Central and Eastern Europe (Czech Republic, Latvia), and all five of these countries being in the top 15% of World Health Organization member states with respect to consumption. Accordingly, alcohol-attributable health harm was high. Implementation of alcohol control policies could be improved, especially the implementation of pricing policies such as taxation increases. A moderate increase of the tax share on alcohol could result in thousands of lives being saved in Central and Eastern Europe in a single year. As taxation increases not only save lives, but also increase state revenue, the implementation of this alcohol control measure should be made a priority.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.385
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2021
Admission routes1
Has abstractyes

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