Building capacity for applied research to reduce tobacco-related harm in low- and middle-income countries: the Tobacco Control Capacity Programme (TCCP)
Bibliographic record
Abstract
BACKGROUND: Tobacco use is the leading cause of preventable deaths in the world. By 2030, more than 80% of these tobacco-related deaths will occur in low- and middle-income countries (LMICs). The aim of the Tobacco Control Capacity Programme (TCCP) therefore, is to reduce tobacco-related mortality and morbidity by building research capacity in LMICs. METHODS: A consortium of fifteen partner organisations across eight countries (Bangladesh, Ethiopia, Ghana, India, South Africa, the Gambia, Uganda and the UK) will offer extensive research methods and leadership training opportunities to conduct high quality research projects on policy and practice and establish strong research partnerships. An example of one such study using a mixed method design to investigate tobacco industry interference in Uganda is presented. RESULTS: The TCCP programme will produce research that can inform policies and practice within countries to prevent or reduce tobacco use. By conducting research in three key areas (tobacco taxation, reducing illicit trade, and addressing tobacco industry interference, as well as other local priorities) the programme will help to reduce tobacco disease and death and also generate revenue for governments through taxation which aids other development priorities. While conducting research in LMICs on these themes TCCP will provide evidence to support better implementation of the Framework Convention for Tobacco Controls (FCTC), which will result in reductions in tobacco-related mortality and morbidity and also help generate revenue for governments through taxation which aids other development priorities. CONCLUSION: The TCCP programme will create a cohort of skilled early-career researchers and research leaders who will build cohesive and successful research teams in LMICs. It will also create several collaborative networks of researchers, policy-makers and advocates to co-produce context-specific research on tobacco control and its translation into policy. This will advance implementation science in LMICs and improve population health. By generating context-specific evidence, the TCCP will support advocacy efforts to shift attitudes within communities and governments towards a stronger tobacco control. Policy makers will be assisted by the evidence generated in this programme to challenge aggressive tobacco industry tactics and implement effective tobacco control.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".