Analysis of alcohol policy in Nigeria: multi-sectoral action and the integration of the WHO “best-buy” interventions
Bibliographic record
Abstract
BACKGROUND: Harmful alcohol use is a modifiable risk factor contributing to the increasing burden of non-communicable diseases and deaths and the implementation of policies focused on primary prevention is pivotal to address this challenge. Policies with actions targeting the harmful use of alcohol have been developed in Nigeria. This study is an in-depth analysis of alcohol-related policies in Nigeria and the utilization of WHO Best Buy interventions (BBIs) and multi-sectoral action (MSA) in the formulation of these policies. METHODS: A descriptive case study design and the Walt and Gilson framework of policy analysis was utilized for the research. Components of the study included a scoping review consisting of electronic search of Google and three online databases (Google Scholar, Science Direct and PubMed) to identify articles and policy documents with no language and date restrictions. Government institution provided documents which were not online. Thirteen policy documents, reports or articles relevant to the policy formulation process were identified. Other components of the study included interviews with 44 key informants (Bureaucrats and Policy Makers) using a pretested guide. The qualitative data were coded and analyzed using thematic analysis. RESULTS: Findings revealed that policy actions to address harmful alcohol use are proposed in the 2007 Federal Road Safety Act, the Non-communicable Diseases Prevention and Control Policy and the Strategic Plan of Action. Only one of the best buy interventions, (restricted access to alcohol) is proposed in these policies. Multi-sectoral action for the formulation of alcohol-related policy was low and several relevant sectors with critical roles in policy implementation were not involved in the formulation process. Overall, alcohol currently has no holistic, health-sector led policy document to regulate the marketing, promotion of alcohol and accessibility. A major barrier is the low government budgetary allocation to support the process. CONCLUSIONS: Nigeria has few alcohol-related policies with weak multi-sectoral action. Funding constraint remains a major threat to the implementation and enforcement of proposed policy actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".