Eliciting preferences for waterpipe tobacco smoking using a discrete choice experiment: implications for product regulation
Bibliographic record
Abstract
OBJECTIVES: Waterpipe smoking is highly prevalent among university students, and has been increasing in popularity despite mounting evidence showing it is harmful to health. The aim of this study was to measure preferences for waterpipe smoking and determine which product characteristics are most important to smokers. SETTING: A large university in the Southeastern USA. PARTICIPANTS: Adult waterpipe smokers attending the university (N=367). DESIGN: Participants completed an Internet-based discrete choice experiment to reveal their preferences for, and trade-offs between, the attributes of hypothetical waterpipe smoking sessions. Participants were presented with waterpipe lounge menus, each with three fruit-flavoured options and one tobacco flavoured option, in addition to an opt out option. Nicotine content and price were provided for each choice. Participants were randomised to either receive menus with a text-only health-warning message or no message. OUTCOME MEASURES: Multinomial and nested logit models were used to estimate the impact on consumer choice of attributes and between-subject assignment of health warnings respectively. RESULTS: On average, participants preferred fruit-flavoured varieties to tobacco flavour. They were averse to options labelled with higher nicotine content. Females and non-smokers of cigarettes were more likely than their counterparts to prefer flavoured and nicotine-free varieties. Participants exposed to a health warning were more likely to opt out. CONCLUSIONS: Fruit-flavoured tobacco and lower nicotine content labels, two strategies widely used by the industry, increase the demand for waterpipe smoking among young adults. Waterpipe-specific regulation should limit the availability of flavoured waterpipe tobacco and require accurate labelling of constituents. Waterpipe-specific tobacco control regulation, along with research to inform policy, is required to curb this emerging public health threat.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.000 |
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".