Targeting mailed nicotine patch distribution interventions to rural regions of Canada: protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Quitting smoking is the most effective way of reducing the risk of cancer among smokers. One way of helping people stop smoking is to provide them with free Nicotine Replacement Therapy (NRT), such as when NRT is sent to people by postal mail as part of a mass distribution initiative. Our previous research indicated that there may be a substantial impact on increasing quit rates of a mailed NRT intervention in rural areas. The current research seeks to confirm this finding and to understand the social determinants of health driving these anticipated large effects. METHODS/DESIGN: Telephone numbers will be randomly selected from across rural regions of Canada in order to recruit adult smokers interested in completing a smoking survey and willing to be interviewed again in 6 months. The survey will ask participants about their smoking history, demographic characteristics, and a hypothetical question: would they be interested in receiving nicotine patches if they were provided to them free of charge? Half of the smokers interested in receiving nicotine patches will be selected by chance and offered the NRT package. The other half of smokers will not be offered the nicotine patches. In addition, the municipality where each participant lives will be identified and, once the relevant general population data becomes available, attempts will be made to link participant data to relevant municipal characteristics (e.g., smoking rates, availability of health services). Characteristics of the participants and the municipalities in which they live will be used to explain why the nicotine patch intervention may have a larger impact in some rural regions compared to others. DISCUSSION: The findings from the proposed RCT are timely and of high relevance as the distribution of nicotine patches has substantial potential to combat the public health problem of cigarette related cancer, other diseases, and premature death from tobacco use. Targeting such tobacco cessation initiatives to rural regions may substantially increase the impact of this intervention, helping to optimize the use of limited prevention resources while aiming to save the maximum number of lives. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov NCT04606797 , October, 27, 2020.
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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 | 0.045 | 0.037 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.103 | 0.016 |
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".