E-Cigarettes are More Addictive than Traditional Cigarettes—A Study in Highly Educated Young People
Bibliographic record
Abstract
E-cigarettes are often considered less addictive than traditional cigarettes. This study aimed to assess patterns of e-cigarette use and to compare nicotine dependence among cigarette and e-cigarette users in a group of highly educated young adults. From 3002 healthy adults, a representative group of 30 cigarette smokers, 30 exclusive e-cigarette users, and 30 dual users were recruited. A 25-item questionnaire was used to collect information related to the patterns and attitudes towards the use of cigarettes and e-cigarettes. The Fagerström test for nicotine dependence (FTND) and its adapted version for e-cigarettes were used to analyze nicotine dependence in each of the groups. The nicotine dependence levels measured with FTND were over two times higher among e-cigarette users (mean 3.5) compared to traditional tobacco smokers (mean 1.6; p < 0.001). Similarly, among dual users, nicotine dependence levels were higher when using an e-cigarette (mean 4.7) compared to using traditional cigarettes (mean 3.2; p = 0.03). Habits and behaviors associated with the use of e-cigarettes did not differ significantly (p > 0.05) between exclusive e-cigarette users and dual users. The findings suggest that e-cigarettes may have a higher addictive potential than smoked cigarettes among young adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".