Assessing use of inhalable nicotine products within complex markets: the dilemma of heated tobacco products
Bibliographic record
Abstract
The introduction of electronic inhalable products, such as nicotine vaping products (NVPs) and heated tobacco products (HTPs), has further diversified the nicotine market landscape. This poses unique challenges in measuring self-reported nicotine use behaviours, which have been the hallmark of tobacco surveillance systems. This paper raises concerns of potential measurement error for electronic inhalable product use in surveys due to similarities in product design between NVPs and HTPs, as well as changing trends in cannabis administration. We identify several strategies for addressing this issue (eg, including descriptive preambles in surveys that differentiate product classes from one another; incorporating survey questions that probe beyond an initial question regarding product use). In the absence of comprehensive validation studies, caution is warranted when interpreting survey results that rely on self-reported HTP use.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".