Web-Assisted Tobacco Interventions: Empowering Change in the Global Fight for the Public’s (e)Health
Bibliographic record
Abstract
Tobacco control in the 21(st) century faces many of the same challenges as in the past, but in different contexts, settings and enabled by powerful new tools including those delivered by information and communication technologies via computer, videocasts, and mobile handsets to the world. Building on the power of electronic networks, Web-assisted tobacco interventions (WATI) provide a vehicle for delivering tobacco prevention, cessation, social support and training opportunities on-demand and direct to practitioners and the public alike. The Framework Convention on Tobacco Control, the world's first global public health treaty, requires that all nations develop comprehensive tobacco control strategies that include provision of health promotion information, population interventions, and decision-support services. WATI research and development has evolved to provide examples of how eHealth can address all of these needs and provide exemplars for other areas of public health to follow. This paper discusses the role of WATI in supporting tobacco control and introduces a special issue of the Journal of Medical Internet Research that broadens the evidence base and provides illustrations of how new technologies can support health promotion and population health overall, empowering change and ushering in a new era of public eHealth.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".