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Record W3207581715 · doi:10.26633/rpsp.2021.142

Assessing Sustainable Development Goal Target Indicator 3.5.2: Trends in alcohol per capita consumption in the Americas 1990–2016

2021· article· en· W3207581715 on OpenAlexaff
Maristela Monteiro, Camila Bertini Martins, Zila M. Sanchez, Jürgen Rehm, Kevin D. Shield, Rachael Falade, Jacqueline MacDiarmid, Pamela J. Trangenstein

Bibliographic record

VenueRevista Panamericana de Salud Pública · 2021
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsPer capitaLatin AmericansAlcohol consumptionConsumption (sociology)Sustainable developmentAlcoholGeographySocioeconomicsEconomic growthEnvironmental healthPolitical scienceEconomicsMedicinePopulationBiology

Abstract

fetched live from OpenAlex

The objective of this study was to estimate trends in alcohol per capita consumption from 1990 to 2016 in the Region of the Americas, covering 35 Member States. Data from the WHO Global Information System on Alcohol and Health were used to calculate the annual percent change of alcohol per capita consumption in each of the 35 countries of the Americas. The Americas as a whole showed no change in the total period, with a slight decrease in the period 2010-2016. From 1990 to 2016, all the countries that presented a trend of annual increase in annual percent change of alcohol per capita consumption were in the Caribbean and Central America. Large increases were found in the recent years in Cuba, Colombia, Uruguay, El Salvador, and several countries of the Non-Latin Caribbean. In conclusion, alcohol use remains a significant obstacle to the achievement of Sustainable Development Goal 3.5. To date, the policy response has been inadequate in protecting the people in the Americas from alcohol-attributable harms. Improving country capacity to collect and analyze data on alcohol per capita consumption is urgently needed to monitor progress on the Sustainable Development Goals and to serve to promote proven alcohol policies for reducing the harmful use of alcohol.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.339
Teacher spread0.303 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueRevista Panamericana de Salud PúblicaSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207