“Don’t Know” Responses for Nicotine Vaping Product Features among Adult Vapers: Findings from the 2018 and 2020 ITC Four Country Smoking and Vaping Surveys
Bibliographic record
Abstract
Nicotine vaping products (NVPs) have evolved rapidly, and some vapers have difficulty reporting about their NVP. NVP knowledge may be important for providing accurate survey data, understanding the potential risks of NVP use, and assessing legal and regulated products. This paper examines current vapers who responded “don’t know” (DK) regarding their NVP features. Data are from adult daily/weekly vapers in Waves Two (2018, n = 4192) and Three (2020, n = 3894) of the ITC Four Country Smoking and Vaping Survey. Analyses assessed DK responses for NVP features (e.g., type/appearance, nicotine) and consumption. A DK index score was computed based on the percent of all features with DK responses, which was tested for associations with demographics, smoking/vaping status, NVP features, purchase location, and knowledge of NVP relative risks. NVP description and appearance were easily identified, but DK was more common for features such as nicotine content (7.3–9.2%) and tank/cartridge volume capacity (26.6–30.0%). DK responses often differed by vaping/smoking status, NVP type/appearance, purchase location, and country. Vapers who are younger, use box-shaped NVPs, purchase online, and exclusive daily vapers were associated with lower DK index scores. Higher DK index scores were associated with poorer knowledge of relative health risks of NVP use. The diversity of the NVP market and wide variation in how products are used makes it challenging to capture information from users about device features, such as nicotine content and capacity, in population surveys.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".