“Everyone Has the Right to Drink Beer”: A Stakeholder Analysis of Challenges to Youth Alcohol Harm-Reduction Policies in Lebanon
Bibliographic record
Abstract
BACKGROUND: Alcohol use is a major risk factor in premature death and disability, especially among youth. Evidence-based policies to prevent and control the detrimental effect of alcohol use have been recommended. In countries with weak alcohol control policies-such as Lebanon, stakeholder analysis provides critical information to influence policy interventions. This paper assesses the views of stakeholders regarding a national alcohol harm reduction policy for youth. METHODS: We interviewed a total of 22 key stakeholders over a period of 8 months in 2015. Stakeholders were selected purposively, to include representatives of governmental and non-governmental organizations and industry that could answer questions related to core intervention areas: affordability, availability, regulation of marketing, and drinking and driving. We analyzed interview transcripts using thematic analysis. RESULTS: Three themes emerged: Inadequacy of current alcohol control policies; weak governance and disregard for rule of law as a determinant of the status quo; and diverting of responsibility towards 'other' stakeholders. In addition, industry representatives argued against evidence-based policies using time-worn strategies identified globally. CONCLUSIONS: Our findings indicate that alcohol harm reduction policies are far from becoming a policy priority in Lebanon. There is a clear need to shift the narrative from victim blaming to structural conditions.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".