Efficient screening of current smoking status in recruitment of smokers for population-based research
Bibliographic record
Abstract
Population-based samples of smokers are necessary for tobacco behavior monitoring and surveillance and for evaluating tobacco control programs and policies. We evaluated the sensitivity and specificity of a simple, one-question screener as a tool to maximize efficiency of obtaining a population representative sample of current smokers. This analysis was based on 5,002 respondents from the Ontario Tobacco Survey (OTS), a regionally stratified longitudinal survey of adult smokers and cross-sectional survey of nonsmokers in Ontario, Canada. Overall, the question "Have you smoked one or more cigarettes in the past six months?" achieved at least 99.7% sensitivity and 87.1% specificity when compared with several standard definitions of current smoking status. The brief screening question minimized respondent burden and data collection costs, and may have had a positive influence on response rate. Having a more conservative measure of smoking status permitted atypical smokers to be included in the survey which will allow us to track their behavior change and evaluate the performance of accepted smoking status definitions. We recommend that studies, which specifically sample smokers, utilize any past 6-month smoking as a brief screener for smoking status.
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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 | 0.094 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".