Methods of the ITC Four Country Smoking and Vaping Survey, wave 1 (2016)
Bibliographic record
Abstract
AIM: To describe the methods of the 2016 International Tobacco Control (ITC) Four Country Smoking and Vaping (4CV) Survey, conducted in 2016 in Australia (AU), Canada (CA), England (EN) and the United States (US). METHODS: The respondents were cigarette smokers, former smokers (quit within the previous 2 years), and at-least-weekly vapers, aged 18 years and older. Eligible cohort members from the ITC Four Country Survey (4C) were retained. New respondents were sampled by commercial firms from their panels. Where possible, ages 18-24 and vapers were oversampled. Data were collected online, and respondents were remunerated. Survey weights were calibrated to benchmarks from nationally representative surveys. RESULTS: Response rates by country for new recruits once invited ranged from 15.2 to 49.6%. Sample sizes for smokers/former smokers were 1504 in AU, 3006 in CA, 3773 in EN and 2239 in the US. Sample sizes for additional vapers were 727 in CA, 551 in EN and 494 in the US. CONCLUSION: The International Tobacco Control Four Country Smoking and Vaping Survey design and data collection methods allow analyses to examine prospectively the use of cigarettes and nicotine vaping products in jurisdictions with different regulatory policies. The effects on the sampling designs and response quality of recruiting the respondents from commercial panels are mitigated by the use of demographic and geographic quotas in sampling; by quality control measures; and by the construction of survey weights taking into account smoking/vaping status, sex, age, education and geography.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".