The Conceptual Model and Methods of Wave 1 ( 2016 ) of the EUREST-PLUS ITC 6 European Countries Survey
Bibliographic record
Abstract
Population-level interventions represent the only real approach for combatting the tobacco epidemic.There is thus great importance in conducting rigorous evaluation studies of tobacco control policies and regulations such as those arising from the WHO Framework Convention on Tobacco Control (FCTC) and the European Union's 2014 Tobacco Products Directive (TPD).The ITC 6 European Countries Survey, a component of the Horizon 2020 Project entitled European Regulatory Science on Tobacco: Policy Implementation to Reduce Lung Disease (EUREST-PLUS), was created to evaluate and impact of the TPD in six EU Member States: Germany, Greece, Hungary, Poland, Romania, and Spain.In each country, a cohort survey of a representative national sample of 1000 smokers was conducted.This paper describes the conceptual model, methodology, and initial survey statistics of Wave 1 of the ITC 6E Survey, which was conducted June-September 2016.The ITC 6E Survey's conceptual model, methodology, and survey instrument, were based on the broader 29-country ITC Project cohort studies, which have been conducted since 2002.The commonality of methods and measures allow a strong potential for cross-country comparisons between the 6 EU countries of the ITC 6E Project and 3 other EU countries (England, France, The Netherlands) in the ITC Project, as well as the broader set of ITC countries outside the EU.
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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 | 0.079 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".