Alcohol policies in Malawi: inclusion of WHO “best buy” interventions and use of multi-sectoral action
Bibliographic record
Abstract
BACKGROUND: Harmful use of alcohol is one of the most common risk factors for Non-Communicable Diseases and other health conditions such as injuries. World Health Organization has identified highly cost-effective interventions for reduction of alcohol consumption at population level, known as "best buy" interventions, which include tax increases, bans on alcohol advertising and restricted access to retailed alcohol. This paper describes the extent of inclusion of alcohol related "best buy" interventions in national policies and also describes the application of multi-sectoral action in the development of alcohol policies in Malawi. METHODS: The study was part of a multi-country research project on Analysis of Non-Communicable Disease Preventive Policies in Africa, which applied a qualitative case study design. Data were collected from thirty-two key informants through interviews. A review of twelve national policy documents that relate to control of harmful use of alcohol was also conducted. Transcripts were coded according to a predefined protocol followed by thematic content analysis. RESULTS: Only three of the twelve national policy documents related to alcohol included at least one "best buy" intervention. Multi-Sectoral Action was only evident in the development process of the latest alcohol policy document, the National Alcohol Policy. Facilitators for multi-sectoral action for alcohol policy formulation included: structured leadership and collaboration, shared concern over the burden of harmful use of alcohol, advocacy efforts by local non-governmental organisations and availability of some dedicated funding. Perceived barriers included financial constraints, high personnel turnover in different government departments, role confusion between sectors and some interference from the alcohol industry. CONCLUSIONS: Malawi's national legislations and policies have inadequate inclusion of the "best buy" interventions for control of harmful use of alcohol. Effective development and implementation of alcohol policies require structured organisation and collaboration of multi-sectoral actors. Sustainable financing mechanisms for the policy development and implementation processes should be considered; and the influence of the alcohol industry should be mitigated.
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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 | 0.043 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".