Partner-Involved Financial Incentives for Smoking Cessation in Dual-Smoker Couples: A Randomized Pilot Trial
Bibliographic record
Abstract
INTRODUCTION: Members of dual-smoker couples (in which both partners smoke) are unlikely to try to quit smoking and are likely to relapse if they do make an attempt. The purpose of this study was to investigate the feasibility, tolerability, and preliminary outcomes of dyadic adaptations of financial incentive treatments (FITs) to promote smoking cessation in dual-smoker couples. AIMS AND METHODS: We enrolled 95 dual-smoker couples (N = 190) in a three-arm feasibility RCT comparing two partner-involved FITs (single vs. dual incentives) against a no-treatment control condition. Participants in all conditions were offered nicotine replacement and psychoeducation. A 3-month follow-up provided information about retention, tolerability (ie, self-reported benefits and costs of the study), and preliminary efficacy (ie, program completion, quit attempts, point-prevalent abstinence, and joint quitting). RESULTS: Results suggest dyadic adaptations were feasible to implement (89% retention rate) and highly tolerable for participants (p < .001). Neither feasibility nor tolerability varied across the treatment arm. Preliminary efficacy outcomes indicated partner-involved FITs have promise for increasing smoking cessation in dual-smoker couples (OR = 2.36-13.06). CONCLUSIONS: Dyadic implementations of FITs are feasible to implement and tolerable to participants. IMPLICATIONS: The evidence that dyadic adaptations of FITs were feasible and tolerable, and the positive preliminary efficacy outcomes suggest that adequately powered RCTs formally evaluating the efficacy of dyadic adaptations of FITs for dual-smoker couples are warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".