Volumetric choice experiment to estimate the impact of e-cigarette and heated tobacco product characteristics on substitution and complementary use among adults who smoke cigarettes and recently initiated e-cigarette use
Bibliographic record
Abstract
BACKGROUND: This study addresses the limited evidence of the impact of product characteristics on demand for and the substitutability of electronic cigarettes (e-cigarettes) or heated tobacco products for combusted cigarettes among people who smoke and have newly begun to use e-cigarettes. METHODS: A sample of 318 adults who smoke and recently initiated/reinitiated e-cigarette use participated in an online volumetric choice experiment in 2020-2021 to assess stated preferences for consumption and own and cross-price elasticities of three e-cigarette options (cig-a-like, vape pen or tank, closed pod system), heated tobacco product (IQOS) and their usual brand of cigarettes. Product attributes manipulated were price, flavour, level of harm, how well the product reduces cravings to smoke, and how discretely the product can be used. Multilevel zero-inflated negative binomial models were used to model the purchased quantities. RESULTS: Cigarettes were preferred over all alternatives. However, demand for cig-a-likes, but not IQOS, increased when cigarette prices were higher. Higher prices for e-cigarettes and IQOS did not increase demand for cigarettes. The odds of buying e-cigarettes/IQOS were higher when their harm was stated as low or unknown versus being similar to cigarettes (ie, very high). Other attributes (including various flavour options) were not significantly associated with demand for e-cigarettes or IQOS. CONCLUSIONS: People who smoke and recently began using e-cigarettes might substitute cig-a-likes for cigarettes when cigarette prices are higher. Policies to increase the cost of combusted cigarettes as well as communicate lower relative harm and low absolute harm of e-cigarettes may facilitate switching behaviour.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".