Methods of the International Tobacco Control (ITC) EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
BACKGROUND: The EUREST-PLUS ITC Europe surveys aim to evaluate the impact of the European Union's Tobacco Products Directive (EU TPD) implementation within the context of the WHO FCTC. This article describes the methodology of the 2016 (Wave 1) and 2018 (Wave 2) International Tobacco Control 6 European (6E) Country Survey in Germany, Greece, Hungary, Poland, Romania and Spain; the England arm of the 2016 (Wave 1) and 2018 (Wave 2) ITC 4 Country Smoking and Vaping (4CV) Survey; and the 2016 (Wave 10) and 2017 (Wave 11) ITC Netherlands (NL) Survey. All three ITC surveys covering a total of eight countries are prospective cohort studies with nationally representative samples of smokers. METHODS: In the three surveys across the eight countries, the recruited respondents were cigarette smokers who smoked at least monthly, and were aged 18 and older. At each survey wave, eligible cohort members from the previous waves were retained, regardless of smoking status, and dropouts were replaced by a replenishment sample. RESULTS: Retention rates between the two waves of the ITC 6E Survey by country were 70.5% for Germany, 41.3% for Greece, 35.7% for Hungary, 45.6% for Poland, 54.4% for Romania and 71.3% for Spain. The retention rate for England between ITC 4CV1 and ITC 4CV2 was 39.1%; the retention rates for the ITC Netherlands Survey were 76.6% at Wave 10 (2016) and 80.9% at Wave 11 (2017). CONCLUSION: The ITC sampling design and data collection methods in these three ITC surveys allow analyses to examine prospectively the impact of policy environment changes on the use of cigarettes and other tobacco products in each country, to make comparisons across the eight countries.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".