Adherence to and Reasons for Premature Discontinuation From Stop-Smoking Medications: Data From the ITC Four-Country Survey
Bibliographic record
Abstract
INTRODUCTION: Nicotine replacement therapies (NRTs) have been demonstrated to be effective in clinical trials but may have lower efficacy when purchased over-the-counter (OTC). Premature discontinuation and insufficient dosing have been offered as possible explanations. The aims are to (a) investigate the prevalence of and reasons for premature discontinuation of stop-smoking medications (including prescription only) and (b) how these differ by type, duration of use, and source (prescription or OTC). METHODS: The sample includes 1,219 smokers or recent quitters who had used medication in the last year (80.5% NRT, 19.5% prescription only). Data were from Waves 5 and 6 of the International Tobacco Control (ITC) Four-Country Survey. RESULTS: Most of the sample (69.1%) discontinued medication use prematurely. This was more common among NRT users (71.4%) than in users of bupropion and varenicline (59.6%). OTC NRT users were particularly likely to discontinue (76.3%). Relapse back to smoking was the most common reason for discontinuation of medication reported by 41.6% of respondents. Side effects (18.3%) and believing that the medication was no longer needed (17.1%) were also commonly reported. Of those who completed treatment, 37.9% achieved 6-month continuous abstinence compared with 15.6% who discontinued prematurely. Notably, 65.6% who discontinued because they believed the medication had worked were abstinent. CONCLUSIONS: Premature discontinuation of stop-smoking medications is common but is not a plausible reason for poorer quit outcomes for most people. Encouraging persistence of medication use after relapse or in the face of minor side effects may help increase long-term cessation outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".