Alcohol policy measures are an ignored catalyst for achievement of the sustainable development goals
Bibliographic record
Abstract
BACKGROUND: By adopting Agenda 2030, governments agreed to review and report on their approach and action for achievement of sustainable development goals annually through the High-Level Political Forum (HLPF) on Sustainable Development. Health and well-being are at the heart of the United Nations Agenda 2030. Given the social and economic harm that can be done by alcohol, reducing the consumption of alcohol is a pre-requisite to achieve the sustainable development goals. We explored how selected European countries have considered alcohol-related harm as an obstacle to achievement of SDGs and the extent to which they view alcohol policy as a solution to the achievement of sustainable development by analysing their voluntary national reviews (VNRs) submitted to the HLPF between years 2016 and 2020. METHODS: We developed our own framework with 260 questions reflecting three dimensions of alcohol-harm considerations: indication, action, and evaluation. We analysed 36 VNRs of 32 European countries by first assessing them against the 260 questions to find out how they report on alcohol harm and whether they, in their action, refer to evidence-based, cost-effective alcohol policy solutions. Afterwards we used content analysis to assess the extent to which the countries addressed alcohol related harm, whether they refer to alcohol harm within SDG 3 (good health and well-being) or look beyond the health goal and consider alcohol harm having impact on goals other than the Goal 3. FINDINGS: Nine countries (28.1%) did not mention alcohol in their report. Only eight countries (25%) mentioned one or more of the alcohol policy best buys among the actions they are taking to reduce alcohol related harm and only three (9.3%) explicitly elaborated on their impact on goals other than goal 3. Only five countries referred to the agreed indicator 3.5.2 measuring alcohol per capita consumption in the adult population. Many of the remaining countries used a range of terminology rather than alcohol per capita consumption, including "excessive use of alcohol", "heavy use", "too much alcohol ", "harmful alcohol consumption", "use among young people". INTERPRETATION: Alcohol use is, for example, associated with violence (SDG 5 and 16), it contributes to inequalities (SDG 5 and 10), it hinders economic growth (SDG 8), disrupts sustainable consumption (SDG 12) and it adversely impacts environment (SDG 13 and 14). The findings of this study show that these effects are not considered in the design of measures to achieve these goals. Moreover, inaccurate language related to alcohol harm indicates a gap in understanding of extend of alcohol burden and the consequences for sustainable development. So does the choice of ineffective measures to reduce alcohol consumption. Education programs and awareness raising campaigns focusing on individual lifestyle are neither in line with WHO Global Strategy to reduce the harm caused by alcohol that all selected countries adopted in 2010, nor do they reflect the seriousness of the problems related to alcohol use. Effective alcohol policy measures, so called three best buys, are missing from the transformative action that the Agenda 2030 calls for and governments committed to.
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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 | 0.062 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".