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Record W1996077332 · doi:10.1159/000072223

Drinking and Its Burden in a Global Perspective: Policy Considerations and Options

2003· article· en· W1996077332 on OpenAlexaff
Robin Room, Kathryn Graham, Jürgen Rehm, David H. Jernigan, Maristela Monteiro

Bibliographic record

VenueEuropean Addiction Research · 2003
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsHarmConsumption (sociology)AbstinenceEnvironmental healthAlcohol consumptionMedicinePublic economicsBusinessAlcoholEconomicsPsychologyPsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

AIMS: To identify the policy implications of the magnitude and characteristics of alcohol consumption and problems, viewed globally, and to summarize conclusions on the effectiveness of the strategies available to policymakers concerned with reducing rates of alcohol problems. DESIGN/METHODS/SETTING: This summative article draws on the findings of the articles preceding it and of reviews of the literature. FINDINGS AND CONCLUSIONS: Overall volume of consumption is the major factor in the prevalence of harms from drinking. Since consumption and associated problems tend to increase with economic development, policymakers in developing economies should be especially aware of the need to develop policies to minimize overall increases in alcohol consumption. Unrecorded consumption is also an important consideration for policy in many parts of the world, and poses difficulties for alcohol control policies. Drinking pattern is also an important contributing factor toward alcohol-related harm. Although some drinking patterns have been shown to produce beneficial health effects, because the net effect of alcohol on coronary disease is negative in most parts of the world, policies that promote abstinence or lower drinking overall may be the safest options. Moreover, sporadic intoxication is common in many parts of the world, and policies are unlikely to change this drinking pattern at least in the short to medium term. At the same time, because injuries comprise a large proportion of the burden of alcohol, it is appropriate to enhance these policies with targeted harm reduction strategies such as drinking and driving countermeasures and interventions focused on reducing alcohol-related violence in specific high-risk settings. Alcohol consumption is a major factor for the global burden of disease and should be considered a public health priority globally, regionally, and nationally for the vast majority of countries in the world. The need for alcohol policy is even stronger when it is taken into consideration that the burden of alcohol estimated in the WHO Global Burden of Disease project includes primarily health problems related to drinking. From the limited evidence available, however, social problems related to drinking seem to impose at least as much burden. Moreover, the burden for both social and health harms fall not only on the drinker, but also on others. There is a broad literature on policy interventions to reduce alcohol problems. Effective strategies include controls over distribution and sale, taxation, drinking-driving countermeasures, brief interventions by health workers or counselors, and selected harm reduction measures. There is a need to develop the growing literature on comparative evaluations of cost-effectiveness of such strategies. In addition, international agreements are needed to support the effectiveness of national strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0030.006
Scholarly communication0.0090.017
Open science0.0020.007
Research integrity0.0140.009
Insufficient payload (model declined to judge)0.0140.002

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 head0.089
GPT teacher head0.409
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations100
Published2003
Admission routes1
Has abstractyes

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