Advancing Tobacco Cessation in LMICs
Bibliographic record
Abstract
Tobacco kills more than 8 million people worldwide every year. Over 80% of the world's 1.3 billion tobacco users live in low- and middle-income countries (LMICs), where the future burden is projected to grow. At the same time, progress in tobacco control has not advanced as far as in many LMICs. In particular, the implementation of tobacco-cessation programs and interventions remains limited. The bulk of the evidence for tobacco-cessation interventions comes from high-income countries and may not reflect the context in LMICs, particularly as resources and training for tobacco cessation are limited. This paper summarizes the current evidence for tobacco-cessation interventions in LMICs and highlights some key challenges and research gaps. Overall, there is a need to build capacity for locally relevant research and implementation science to support tailored cessation interventions and strategies for LMICs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".