Tobacco control funding for low-income and middle-income countries in a time of economic hardship
Bibliographic record
Abstract
OBJECTIVE: To assess how levels of tobacco control funding for low-income and middle-income countries (LMIC) changed following the 2008-2009 global economic downturn. METHODS: In order to estimate the amount of tobacco control funding in LMICs, we created an integrated database of Development Assistance to Control Tobacco (DACT). This database includes data on funding from bilateral and multilateral donors, non-governmental organisations, private foundations and the corporate sector. The database contains information on 1389 disbursements awarded by 30 entities between 2000 and 2012. RESULTS: DACT declined only marginally from US$68.8 million (US$0.016 per adult) in 2009 to US$68.2 million (US$0.016 per adult) in 2011, but deviated significantly from its 2000 to 2009 trend. The sources of funding remain highly concentrated, with nearly a half of the money coming from the Bloomberg Initiative and the Bill & Melinda Gates Foundation in 2011. The relative importance of institutional and research grants has declined. CONCLUSIONS: Our findings are consistent with the patterns in general levels of development assistance for health: after a decade of rapid growth, funding for tobacco control activities in LMICs has levelled off. Just as the tobacco control community is beginning to envision the endgame for tobacco, the funding remains erratic, inadequate, and highly vulnerable due to its level of concentration. Innovative financing mechanisms might help to increase the funding pool.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".