Alcohol policy development in Sierra Leone: An assessment of the role of civil society
Bibliographic record
Abstract
Objective: To assess the value addition of civil society collaboration with Government in the development of Sierra Leone’s National Alcohol Policy (NAP). Policy Development Process: We reviewed the entire process of lobby, advocacy, and support for the development of the NAP from 2015 when the Siera Leone Alcohol Policy Alliance (SLAPA) was formed to the launch of the NAP in 2023. It also assesses the level of collaboration between FoRUT, SLAPA and Ministry of Health of Health and Sanitation (MoHS). The MoHS coordinated the policy formulation process with substantive technical support from civil society. Results: The quality of the NAP was rated high as it reflected appropriate policy areas and interventions from the Global Strategy to Reduce the Harmful Use of Alcohol, the WHO SAFER initiative, and the Global Action Plan for Alcohol Control (202-2030). The NAP is a solid reference material for the development of a new alcohol bill. MoHS recognized FoRUT and SLAPA as the national champions for alcohol control in the country. FoRUT, directly and through SLAPA influenced the process of developing the NAP and its quality through advocacy, collaboration and technical and financial support. Conclusions: The development of the NAP in Sierra Leone truly reflects civil society-government collaboration, built on mutual trust and a common health and development agenda. Connecting national level lobby and advocacy with regional global advocacy actions to prioritize, influence, support, and monitor the alcohol control agenda is a critical catalyst for civil society to advancing the development of NAPs.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".