Methods of the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
This paper outlines the design features, data collection methods and analytic strategies of the International Tobacco Control (ITC) Four Country Survey, a prospective study of more than 2000 longitudinal respondents per country with yearly replenishments. This survey possesses unique features that sets it apart among surveys on tobacco use and cessation. One of these features is the use of theory-driven conceptual models. In this paper, however, the focus is on the two key statistical features of the survey: longitudinal and "quasi-experimental" designs. Although it is often possible to address the same scientific questions with a cross-sectional or a longitudinal study, the latter has the major advantage of being able to distinguish changes over time within individuals from differences among people at baseline (that is, differences between age and cohort effects). Furthermore, quasi-experiments, where countries not implementing a given new tobacco control policy act as the control group to which the country implementing such a policy will be compared, provide much stronger evidence than observational studies on the effects of national-level tobacco control policies. In summary, application of rigorous research methods enables this survey to be a rich data resource, not only to evaluate policies, but also to gain new insights into the natural history of smoking cessation, through longitudinal analyses of smoker behaviour.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".