Lessons Learned from Twelve Years of Partnered Tobacco Cessation Research in the Dominican Republic
Bibliographic record
Abstract
Engaging partners for tobacco control within low and middle income countries (LMICs) at early stages of tobacco control presents both challenges and opportunities in the global effort to avert the one billion premature tobacco caused deaths projected for this century. The Dominican Republic (DR) is one such early stage country. The current paper reports on lessons learned from 12 years of partnered United States (US)-DR tobacco cessation research conducted through two NIH trials (Proyecto Doble T, PDT1 and 2). The projects began with a grassroots approach of working with interested communities to develop and test interventions for cessation and secondhand smoke reduction that could benefit the communities, while concurrently building local capacity and providing resources, data, and models of implementation that could be used to ripple upward to expand partnerships and tobacco intervention efforts nationally. Lessons learned are discussed in four key areas: partnering for research, logistical issues in setting up the research project, disseminating and national networking, and mentoring. Effectively addressing the global tobacco epidemic will require sustained focus on supporting LMIC infrastructures for tobacco control, drawing on lessons learned across partnered trials such as those reported here, to provide feasible and innovative approaches for addressing this modifiable public health crisis.
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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 | 0.064 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".