Organizational structure, capacity and reach of organizations involved in alcohol prevention: An assessment of stakeholders across five countries in East Africa
Bibliographic record
Abstract
Aims: East African countries, classified as low- and middle-income countries (LMICs), are disproportionately harmed by alcohol consumption, and many countries lack strategies to address and prevent alcohol harm. This study draws on community input from stakeholders involved in alcohol harm prevention in five East African countries to identify organizational structures, capacity and outreach, and strategies for capacity building to address the high burden of alcohol harm more systematically. Design/Setting/Participants: A cross-sectional survey was distributed in 2020 by the East Africa Alcohol Policy Alliance to their member alliances and stakeholders across five countries in East Africa (i.e., Burundi, Kenya, Rwanda, Tanzania and Uganda). Analyses were computed based on 171 persons/organizations completing the survey. Measures: The East Africa Alcohol Policy Alliance Capacity Assessment Survey (EAAPACAS) included organizational size and funding, research capacity, priorities, and perceptions related to alcohol prevention and harm locally and nationally. Results: The types of organizations, funding structures, and functions dedicated to alcohol prevention vary widely across countries, indicating great diversity and heterogeneity of organizations working on alcohol prevention and advocacy in East Africa. Most organizations relied on volunteer staff. Additionally, 51% reported that they did not know or did not meet their program needs with the available operational funds. Conclusion: These organizations rely primarily on volunteers and face significant barriers in order to achieve their goals with their current budget, primarily derived from foundations and private donations. Overall, these findings indicate that the infrastructure for alcohol prevention is weak and fragmented in countries where national initiatives are limited or underfunded.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".