Survey Methods of the 2018 International Tobacco Control (ITC) Japan Survey
Bibliographic record
Abstract
This paper describes the methods of the Wave 1 (2018) International Tobacco Control (ITC) Japan Survey. The respondents were adults aged 20 years and older in one of four user groups: (1) cigarette-only smokers who smoked at least monthly and used heated tobacco products (HTPs) not at all or less than weekly, (2) HTP-only users who used HTPs at least weekly and smoked cigarettes not at all or less than monthly, (3) cigarette-HTP dual users who smoked at least monthly and used HTPs at least weekly, and (4) non-users who had never smoked or who smoked less than monthly and used HTPs less than weekly. Eligible respondents were recruited by a commercial survey firm from its online panel. Respondents were allocated proportionally to sample strata based on demographic, geographic, and user type specifications benchmarked to a national reference. Survey weights, accounting for smoking/HTP use status, sex, age, education, and geography, were calibrated to benchmarks from a nationally representative survey in Japan. Response rate was 45.1% and cooperation rate was 96.3%. The total sample size was 4615 (3288 cigarette smokers, 164 exclusive HTP users, 549 cigarette-HTP dual users, and 614 non-users). The 2018 ITC Japan Survey sampling design and survey data collection methods will allow analyses to examine prospectively the use of cigarettes and HTPs in Japan and factors associated with the use of both products and of transitions between them.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".