Adherence to and Reasons for Premature Discontinuation From Stop-Smoking Medications: Data From the ITC Four-Country Survey
Why this work is in the frame
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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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it