Methods of the 2020 (Wave 1) International Tobacco Control (ITC) Korea Survey
Bibliographic record
Abstract
This article presents methods used in the 2020 International TC Korea Wave 1 (KRA1) Survey. To date, three cohorts of Korean respondents have participated in the larger ITC Korea Project (cohort 1: 2005-2014, cohort 2: 2016, and cohort 3: 2020-present). The overall objectives of the ITC KRA1 Survey were to examine the use of cigarettes, heated tobacco products (HTPs), e-cigarettes (ECs); whether HTPs might help smokers quit; and the effectiveness of tobacco control policies, such as large graphic warnings, high cigarette taxes, and smoking bans in public places. The KRA1 Survey measures were identical or functionally similar to those of the ITC Japan Survey and, to a lesser extent, those of other ITC countries. Key measures assessed sociodemographic characteristics of respondents; the use of combustible cigarettes, e-cigarettes, and heated tobacco products; and measures assessing policies of the WHO Framework Convention on Tobacco Control, including price and tax (Article 6), smoke-free laws (Article 8), health warnings (Article 11), education, communication and public awareness (Article 12), advertising, promotion, and sponsorship restrictions (Article 13), and support for cessation (Article 14). Adult tobacco and/or nicotine users aged ≥19 years in South Korea were recruited by a commercial survey firm from its online panel. Overall, 4794 respondents completed the KRA1 Survey. The cooperation rate was 97.4% and the response rate was 15.2%. The cohort design permits assessment of transitions in products used among users in South Korea and evaluations of the impact of policies on tobacco and/or nicotine products used and transitions in use.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".