Methods of the 2020 (Wave 1) International Tobacco Control(ITC) Malaysia survey
Bibliographic record
Abstract
The ITC Malaysia Project is part of the 31-country ITC Project, of which the central objective is to evaluate the impact of tobacco control policies of the WHO Framework Convention on Tobacco Control (FCTC). This article describes the methods used in the 2020 International Tobacco Control (ITC) Malaysia (MYS1) Survey. Adult smokers and non-smokers aged ≥18 years in Malaysia were recruited by a commercial survey firm from its online panel. Survey weights, accounting for smoking status, sex, age, education, and region of residence, were calibrated to the Malaysian 2019 National Health and Morbidity Survey. The survey questions were identical or functionally similar to those used in other ITC countries. Questions included demographic measures, patterns of use, quit history, intentions to quit, risk perceptions, beliefs and attitudes about cigarettes, e-cigarettes, and heated tobacco products. Questions also assessed measures assessing the impact of tobacco demand-reduction domains of the FCTC: price/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). The total sample size was 1253 (1047 cigarette smokers and 206 non-smokers). Response rate was 11.3%, but importantly, the cooperation rate was 95.3%. The 2020 ITC MYS1 Survey findings will provide evidence on current tobacco control policies and evidence needed by Malaysian government regulatory agencies to develop new or strengthen existing tobacco control efforts that could help achieve Malaysia's endgame, i.e. a tobacco-free nation by 2040.
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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 | 0.002 |
| 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) | 0.016 | 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".