Dual users’ perceptions of the addictive properties of cigarettes versus E-cigarettes
Bibliographic record
Abstract
INTRODUCTION: Electronic cigarettes ("e-cigarettes") are commonly promoted as a less-harmful alternative to combustible cigarettes, yet many individuals concurrently use both products ("dual users"). Little is known about the extent to which dual users' perceptions of the addictive properties of these products differ, or to what extent there are differences in the factors that elicit craving for each product. METHODS: An online survey evaluated beliefs about the addictive properties of cigarettes vs e-cigarettes and the situational and affective precipitants of product craving, on a scale from 1 to 10, in a sample of Canadian adults that reported past-month use of combustible and e-cigarettes (N = 175; 79 female). RESULTS: Participants rated cigarettes as more addictive than e-cigarettes, and on average reported higher levels of dependence on combustible cigarettes. While the addictive properties of both combustible and e-cigarettes were largely attributed to nicotine, non-nicotine factors (e.g. flavouring, other non-nicotine ingredients) were believed to make a relatively stronger contribution to the addictive properties of e-cigarettes, particularly among women. Participants reported greater increases in craving for combustible cigarettes in response to negative affective states and situational factors, and these effects were strongest among participants that displayed greater dependence on combustible tobacco relative to e-cigarettes. CONCLUSIONS: Dual users perceived cigarettes to be more addictive than e-cigarettes and attributed the addictive properties of each product to different factors. Further, cravings for combustible cigarettes were more strongly linked to certain negative affective states and situational factors relative to e-cigarettes. Findings suggest that there may be limited substitutability between combustible and e-cigarettes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".