Impact of large-scale distribution and subsequent use of free nicotine patches on primary care physician interaction
Bibliographic record
Abstract
BACKGROUND: Large-scale distribution efforts of free nicotine replacement therapy (NRT) have been documented to be cost-effective interventions for increasing smoking quit rates. However, despite nearly a dozen studies evaluating their effectiveness, none have examined whether free NRT provision promotes further primary care help-seeking and the impact that it may have on cessation efforts. METHODS: In the context of a randomized controlled trial, a secondary analysis was conducted on 1000 adult regular smokers randomized to be mailed a 5-week supply of nicotine patches or to a no intervention control group. Recipients and users of free nicotine patches at an 8 week follow-up were successfully case matched to controls based on age, gender, baseline level of nicotine dependence and intent to quit (n = 201 per group). Differences in physician interaction between the two groups were evaluated at both 8 week and 6 month follow-ups. The impact of physician interaction on self-reported smoking abstinence at each follow-up was also examined. RESULTS: Although no differences in physician interaction were noted between groups at the 8 week follow-up, at the 6 month follow-up, nicotine patch users reported greater frequency of discussing smoking with their physician (43.9%), as compared to the control group (30.3%) (p = 0.011). Across both groups, over 90% of those that discussed smoking with a physician were encouraged to quit and approximately 70% were provided with additional support. Separate ANOVAs revealed no significant impact of physician interaction on cessation (p > 0.05), regardless of group or follow-up period, however, at the 6 month follow-up, nicotine patch users who discussed cessation with a physician had made serious quit attempts at significantly greater rates (72.6%), compared to controls (49.1%) (p = 0.007). CONCLUSIONS: Irrespective of group, the majority of smokers in the present study did not discuss cessation with their physician. Recipients and users of nicotine patches however, were more likely to discuss smoking with their physician, suggesting that the provision of free NRT particularly to those who are likely to use it may facilitate opportunities for benefits beyond the direct pharmacological effects of the medication. TRIAL REGISTRATION: clinicaltrials.gov , NCT01429129 . Registered: 2 September 2011.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".