Heterogeneity in the Effects of Reward- and Deposit-based Financial Incentives on Smoking Cessation
Bibliographic record
Abstract
RATIONALE: Targeting different smoking cessation programs to smokers most likely to quit when using them could reduce the burden of lung disease. OBJECTIVES: To identify smokers most likely to quit using pure reward-based financial incentives or incentive programs requiring refundable deposits to become eligible for rewards. METHODS: We conducted prespecified secondary analyses of a randomized trial in which 2,538 smokers were assigned to an $800 reward contingent on sustained abstinence from smoking, a refundable $150 deposit plus a $650 reward, or usual care. MEASUREMENTS AND MAIN RESULTS: Using logistic regression, we identified characteristics of smokers that were most strongly associated with accepting their assigned intervention and ceasing smoking for 6 months. We assessed modification of the acceptance, efficacy, and effectiveness of reward and deposit programs by 11 prospectively selected demographic, smoking-related, and psychological factors. Predictors of sustained smoking abstinence differed among participants assigned to reward- versus deposit-based incentives. However, greater readiness to quit and less steep discounting of future rewards were consistently among the most important predictors. Deposit-based programs were uniquely effective relative to usual care among men, higher-income participants, and participants who more commonly failed to pay their bills (all interaction P values < 0.10). Relative to rewards, deposits were more effective among black persons (P = 0.022) and those who more commonly failed to pay their bills (P = 0.082). Relative to rewards, deposits were more commonly accepted by higher-income participants, men, white persons, and those who less commonly failed to pay their bills (all P < 0.05). CONCLUSIONS: Heterogeneity among smokers in their acceptance and response to different forms of incentives suggests potential benefits of targeting behavior-change interventions based on patient characteristics. Clinical trial registered with www.clinicaltrials.gov (NCT 01526265).
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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 | 0.056 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".