Creating a global tobacco control treaty surveillance platform
Bibliographic record
Abstract
Introduction Over 180 countries have been reporting their progress on the implementation of the WHO Framework Convention on Tobacco Control since 2008. However these important datasets have not been consolidated, assembled and organized in an online, functional, user-friendly manner that can be readily accessed by all tobacco control stakeholders. The absence of a consolidated online data surveillance platform has constrained treaty monitoring, reporting and implementation. Material and Methods To address this challenge, ASH Canada and the Institute for Global Tobacco Control at Johns Hopkins Bloomberg School of Public Health created a robust, interactive online treaty monitoring platform (www.globaltobaccocontrol.org/progresshub). The contents, capabilities, features and functions of the platform were determined in consultation with an advisory committee consisting of 15 international experts and based on the availability and contents of reporting datasets and the capabilities and limitations of the chosen data analytics software application (Tableau). Results The Global Tobacco Control Progress Hub contains over 300 tobacco control indicators from over 180 countries spanning up to 12 years of reporting and representing over 400,000 datapoints. The Progress Hub includes four dashboards that allow for various data groupings, breakdowns and comparisons by country, WHO region, national personal income level and human development index gradients. The platform also includes national scoring, ranking and longitudinal results for each reporting country and the ability to compile national FCTC shadow reports. Conclusions The Global Progress Hub provides a new window on the world of FCTC implementation by providing tobacco control stakeholders with online access to the major treaty implementation datasets. This innovative open data platform allows for enhanced monitoring surveillance, reporting and implementation of the treaty.
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 | 0.001 |
| 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) | 0.000 | 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 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".