Mapping the African Tobacco Control Network
Bibliographic record
Abstract
ABSTRACT Background To understand the state of tobacco control efforts across Africa, a first-ever survey was implemented to assess the nature and activities of tobacco control stakeholders across the African continent. Methods A survey in English, Arabic, and French was made available to individuals and organizations to assess the types and scope of tobacco control efforts and experience with tobacco control programs based on FCTC articles/MPOWER components. Results There were 219 respondents from 32 African and 6 non-African countries. Research and advocacy were the most reported activities, and several organizations emerged as network nodes for connecting tobacco control efforts across multiple African countries. The most common FCTC articles/MPOWER components worked on were (W) warning about the dangers of tobacco (58%), (M) monitor tobacco use and policies (49%), and (P) protection against secondhand smoke exposure (47%). Significant between-country differences were also found on some FCTC articles/MPOWER components: (1) (R) price and tax measures [Articles 6 and 15] (F=1.57, p=0.048), (2) industry interference [Article 5.3] (F=1.62, p=0.038), and (3) economically viable alternatives to tobacco growing [Article 17] (F=1.94, p=0.007). Discussion This study found a broad and robust tobacco control network across Africa, with multiple organizations serving those networks and having overlapping collaborations. There is considerable variability in tobacco control priorities and networking, and multiple barriers were identified to expanding the network and to fostering increased tobacco control efforts. The results point to important directions for increasing collaboration across FCTC articles/MPOWER components to improve tobacco control efforts; potential research opportunities, including an analysis of tobacco industry activities, an exploration of ways to help people quit tobacco, and approaches to elevate the cost of tobacco; and a solid tobacco control network foundation on which to build. However, exploring creative approaches to increase research most relevant to specific countries and their cultural characteristics is essential.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".