Evaluating the progress of alcohol policies in Burundi against the WHO ‘best buy’ interventions: implications for public health.
Bibliographic record
Abstract
Introduction: Alcohol use is a major global health risk, with Global South countries experiencing greater harm per litre of alcohol consumed than those in the Global North. In Burundi, a country with a low-income economy, 16.6% of people aged 15 and above binge drink, and over 30% of women drink during pregnancy. This paper examines current alcohol policies in Burundi, how well they match the WHO ‘best buy’ policy options, and stakeholder views on their implementation. Methods: We searched for policy documents via online searches, visits to government offices, and snowball sampling from contact with key stakeholders. Semi-structured interviews were conducted with ten stakeholders. The WHO-European Action Plan to Reduce the Harmful Use of Alcohol (EAPA) tool was used to analyse the extent to which Burundi has adopted recommended policy standards. Interviews were thematically analysed using NVivo software. Results: Only nine of the 34 WHO-EAPA indicators are addressed, seven out of 34 indicators are mentioned with no clear actions, and 18 are not addressed in the eight policy documents that met our inclusion criteria. The large proportion of indicators absent from Burundi policy relate to availability, pricing and taxation, drinking-driving, taxation, and marketing. An absence of legislation to support existing policies, industry interference, corruption, and cultural norms around alcohol were identified as key barriers to implementation. Conclusions: Burundi should enact laws to support existing policies and design regulations targeting marketing and advertising. Government and civil society coalitions should report and address any alcohol industry influence in policymaking and implementation.
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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 | 0.131 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".