Impact of self-reported lifetime depression or anxiety on effectiveness of mass distribution of nicotine patches
Bibliographic record
Abstract
BACKGROUND: Large-scale public health initiatives providing free nicotine replacement therapy have been shown to increase smoking cessation rates; however, their effectiveness among the highly prevalent population of smokers with depression and anxiety disorders has not been explored. The aim of this study was to investigate the influence of lifetime history of depression or anxiety on smoking cessation success following the free distribution of nicotine patches. METHOD: In the context of a randomised controlled trial, a secondary analysis was conducted on 1000 adult regular smokers randomised to be mailed a 5-week supply of nicotine patches or to a no intervention control group. Participants were divided into subgroups based on the presence of self-reported lifetime diagnosis of depression and anxiety. RESULTS: Irrespective of self-reported lifetime history of depression or anxiety, odds of self-reported cessation at 6 months were significantly greater among groups receiving nicotine patches compared to no intervention control (no history of depression or anxiety: OR 2.20; 95% CI 1.05 to 4.63; history of depression or anxiety present: OR 3.90; 95% CI 1.28 to 11.88). Among nicotine patch recipients only, quit outcomes did not differ between those with and without self-reported lifetime depression or anxiety in models unadjusted and adjusted for differences in demographic and smoking characteristics. CONCLUSIONS: The mass distribution of free nicotine patches (without behavioural support) is effective among smokers with or without lifetime history of depression or anxiety alike, providing further support for the adoption of similar initiatives as a means of promoting tobacco cessation on a population level. TRIAL REGISTRATION NUMBER: NCT01429129, Post-results.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".