Community awareness and engagement to prevent alcohol related harm: Stakeholder priorities in West Africa
Bibliographic record
Abstract
Aims: West Africa is disproportionately harmed by alcohol consumption. However, limited information is available about the alcohol prevention strategies used by stakeholders in West Africa. In addition, there is scant awareness of health consequences from alcohol use among the communities with which stakeholders engage in alcohol prevention. Design/Setting/Participants: A cross-sectional survey was distributed in 2020 by the West African Alcohol Policy Alliance to their member alliances and stakeholders across nine countries. Analyses were computed based on 171 persons/organizations completing the survey. Measures: The West Africa Alcohol Policy Alliance Capacity Assessment Survey (WAAPACAS) included questions about programs and service delivery, alcohol prevention strategies used, and community knowledge of alcohol as a risk factor for a range of health concerns. Results: In terms of addressing alcohol-related harm, non-governmental organizations (NGOs) and community-based organizations (CBOs) across West Africa engage primarily in community outreach and health promotion activities. Even so, awareness of alcohol as a risk factor for key health conditions remains relatively low, and varies by country and acute versus longer term consequences. Conclusion: Leveraging the outreach and engagement by NGOs/CBOs will be critically important for addressing alcohol-related harm in West Africa. However, NGOs/CBOs will need additional capacity and information to convey that alcohol is a key risk factor for several health outcomes to ensure communities are more informed about the range of alcohol-related harms.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".