Real-world Effectiveness of Evidence-based Smoking Cessation Approaches for Vaping Reduction and Cessation
Bibliographic record
Abstract
This study explored vaping cessation and reduction in the Smoking Treatment for Ontario Patients (STOP) program. Participants received nicotine replacement therapy and behavioral support, divided into two groups: quitting e-cigarettes only (ECQ) and quitting both e-cigarettes and tobacco cigarettes (ETQ). The primary outcome was 7-day point prevalence e-cigarette abstinence at six months, while the secondary outcome focused on reducing vaping frequency. Out of 424 participants, 40.5% of ECQ and 42.5% of ETQ achieved e-cigarette cessation, with no significant difference between groups. There was a significant shift in the distribution of vaping frequency over time (χ² (15, N = 116) = 46.731, p < 0.001), confirming a significant reduction in vaping frequency among participants who did not quit at 6 months. Future studies should focus on tailored cessation programs considering the specific nicotine products and demographic contexts of users.
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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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".