Targeted distribution of nicotine patches by mail to rural regions of Canada: Predictors of patch use
Bibliographic record
Abstract
INTRODUCTION: Rural regions generally report higher smoking rates than urban centers, which increases the risk of tobacco related harms and consequences, and makes promoting smoking cessation in these areas a priority. Mass distribution of nicotine replacement therapy (NRT) by postal mail has been found to increase the odds of successful cessation attempts. Understanding factors that contribute to the use of NRT could help maximize this intervention's effectiveness. METHODS: People who smoke cigarettes and live in rural areas of Canada were recruited from December 2020 to February 2022 using random digit telephone dialing. Participants were either randomized to be mailed a free, 5-week supply of NRT patches (experimental condition; n=252) or not (control condition; n=246). This secondary analysis used data from this randomized controlled trial to conduct an ordinal regression to determine if any variables measured at baseline predicted which participants in the experimental condition used none, some, or all of the NRT patches. RESULTS: Greater confidence in ability to quit (AOR=1.07; 95% CI: 1.00-1.15) independently predicted more patch use, while living in more remote places (AOR=0.25; 95% CI: 0.07-0.90) and past substance use (compared to having no history) (AOR=0.68; 95% CI: 0.45-1.04) independently predicted less use. CONCLUSIONS: Understanding what contributes to NRT use in rural mass distribution programs could help maximize the odds of successful cessation attempts, personalize treatment recommendations, and target limited rural resources. Future research focused on rural NRT use and smoking cessation is merited.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".