Evaluating partner-involved financial incentive treatments for smoking cessation in dual-smoker couples: A randomized controlled trial protocol
Bibliographic record
Abstract
BACKGROUND: Individuals who smoke combustible nicotine cigarettes and are partnered with another smoker are less likely to quit smoking than other smokers. Financial incentives for cessation increase individual abstinence. However, these interventions have not been examined using a dyadic approach that targets the couple rather than an individual. The current study will evaluate two variants of a dyadic financial incentive intervention for smoking cessation in dual-smoking couples. METHODS: Feasibility and tolerability of dyadic financial incentive treatments in dual-smoking couples has been established, along with their preliminary efficacy. The current study will utilize a large-scale clinical trial to systematically evaluate the efficacy and mechanisms of change of two variants of dyadic financial incentive interventions among 450 dual-smoking couples. RESULTS: The primary study outcome will be 6-month point-prevalence abstinence (self-report abstinence for 7 days + biochemical verification) among targets in dual-smoking couples. Partner abstinence at 6-months, and both target and partner abstinence at 1, 3, and 12 months will be examined as secondary outcomes. Potential treatment mechanisms, including motivational and relationship processes will be evaluated to inform future interventions. DISCUSSION: Individuals with smoking partners experience low motivation to quit and low success rates when they make quit attempts. Current evidence-based interventions target individuals in isolation from their social networks that strongly influence them. This approach represents a critical next step toward developing interventions that effectively capitalize on social network members to produce and sustain smoking cessation.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".