Contextual factors impacting WHO Framework Convention on Tobacco Control implementation in Africa—a scoping review
Bibliographic record
Abstract
According to the World Health Organization (WHO), tobacco use causes over 8 million deaths annually including 1.3 million due to second-hand exposure. Furthermore, data from the Tobacco Atlas show that the tobacco industry continues to target new markets in the WHO African region, one of two regions where absolute numbers of smokers continue to increase. Understanding context contributes to policy formulation and implementation ensuring relevance to a country's political economy. Focusing on the WHO African region, this scoping review (i) maps the extent of academic research examining contextual factors on the WHO Framework Convention on Tobacco Control (WHO FCTC) national-level implementation, and (ii) reports on contextual factors impacting the WHO FCTC implementation. Using a stepwise structured approach, we conducted a search across four academic databases, yielding 10 342 articles and 42 were selected for full data extraction. Leichter's four categories of context (situational, structural, cultural and exogenous) and the stages of heuristic policy model guided data extraction. Study findings indicated that situational contextual factors such as the burden of disease or its impact on health can push governments toward policy formulation. Structural contextual factors included political considerations, economic interests, funding, institutional congruence, strength of policy and institutional capacity as important. Cultural contextual factors included the influence of policy entrepreneurs, current social trends and public opinion. Exogenous contextual factors included the WHO FCTC, tobacco industry influence at the national-level and bi-lateral partnerships. Further understanding contextual factors affecting the WHO FCTC national implementation can strengthen policy formulation and align required support with the WHO FCTC Secretariat and other relevant bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".