Alcohol prevention research and policy development in LMICs: New perspectives, insights and recommendations
Bibliographic record
Abstract
Alcohol prevention, research, and policy development in LMICs: New perspectives, insights and recommendations EditorialAlcohol consumption presents a significant public health challenge globally and the World Health Organization (WHO, 2024) prioritizes evidence-based strategies and highimpact policies to reduce alcohol harm.Yet, substantial expansions in alcohol research, capacity, and policy development are urgently needed in many regions of the world to continue to build the evidence base and make progress.This special issue includes eight articles addressing alcohol prevention research and recommendations for policy development specifically in low-and middle-income countries (LMICs).
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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 | 0.047 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".