A Systematic Review and Meta-analysis of Associations between E-cigarette Use among Nontobacco Users and Combustible Cigarette Smoking Intentions
Bibliographic record
Abstract
Objectives: The potential association between e-cigarette use and cigarette smoking persists as an important public health issue. Strictly adhering to AMSTAR 2 and PRISMA guidelines, our evidence synthesis examined the association between e-cigarette use among non-tobacco users and the intention to smoke cigarettes. Methods: We searched 3 databases from January 1, 2007 to April 26, 2023 and screened search results according to the PICOS review method. Results: We synthesized 20 demographically adjusted studies on smoking intention, including related outcome measures of willingness/openness/curiosity to smoke cigarettes and susceptibility to smoke cigarettes. All studies suggested a significant association between e-cigarette use and intention to smoke according to measures of experimental e-cigarette use (i. e., ever or current use, and not established and/or regular use). Conclusions: Studies defining e-cigarette use according to measures of established and/or regular use, and that adequately controlled for specific confounding variables representing common liabilities between e-cigarette use and cigarette smoking are limited in the evidence base. Thus, currently there is limited evidence to support an association between e-cigarette use and the intention to smoke cigarettes. Future research should apply measures of regular and/or established e-cigarette use that adequately account for confounding variables that consider common liabilities between e-cigarette use and cigarette smoking. Doing so would enable the findings to support robust determinations regarding any potential association between e-cigarette use and the intention to smoke cigarettes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".