ITC “spit and butts” pilot study: The feasibility of collecting saliva and cigarette butt samples from smokers to evaluate policy
Bibliographic record
Abstract
INTRODUCTION: Large-scale epidemiological surveys have frequently relied upon clinic-based sample collection to incorporate biological data, which can be costly and result in nonrepresentative data. Collecting samples in a nonclinical setting (i.e., through postal mail or at the subject's home) offers an alternative option that is minimally invasive and can be incorporated into large population-based studies. OBJECTIVES: (a) To assess the feasibility of collecting biological data from a cohort of smokers in the International Tobacco Control (ITC) study, through the mail and in the home; (b) to examine whether participants are representative of the population under consideration; and (c) to evaluate how the added burden of providing biomarker samples might impact subsequent participation in a follow-up survey. METHODS: Participants were asked to provide a saliva sample and five cigarette butts from cigarettes smoked on a single day, using standardized procedures. Sample collection kits were mailed to a random sample of 400 daily cigarette smokers who were involved in the 2006 annual ITC Four Country (United Kingdom, United States, Canada, and Australia) telephone survey and agreed to participate in sample collection. A random sample of 179 daily smokers who participated in a face-to-face ITC survey in Mexico and Uruguay and agreed to participate in sample collection were also asked to provide samples. RESULTS: Samples were collected from 96% of invited participants in the face-to-face surveys and 52% of participants in the telephone survey. The added burden of the sample collection did not reduce survey retention rates. Participants who initially agreed to participate in the sample collection were more likely to participate in the subsequent survey than participants who were not asked or declined to participate (odds ratio [OR] = 1.28; 95% CI = 1.01-1.62, p = .021). Further, those who provided samples were also more likely to participate in the subsequent survey than those who did not (OR = 2.78; 95% CI = 1.71-4.52, p < .001). DISCUSSION: Collecting saliva and cigarette butt samples from a group of smokers is feasible, yields a representative sample, and the added participant burden does not reduce subsequent survey response rates.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".