Assessing Sustainable Development Goal Target Indicator 3.5.2: Trends in alcohol per capita consumption in the Americas 1990–2016
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".