Longitudinal Care Improves Cessation in Smokers Who Do Not Initially Respond to Treatment by Increasing Cessation Self-Efficacy, Satisfaction, and Readiness to Quit: A Mediated Moderation Analysis
Bibliographic record
Abstract
BACKGROUND: The Tobacco Longitudinal Care study was a randomized controlled trial for smoking cessation. It demonstrated that longitudinal care for smoking cessation, in which telephone-based counseling and nicotine replacement therapy were offered for 12 months, was more effective than the standard 8-week treatment. PURPOSE: This study aims to identify for whom and how longitudinal care increased the likelihood of abstinence. METHODS: Mediated moderation analyses were utilized across three time points. RESULTS: There was a trend towards smokers who did not respond to treatment (i.e., were still smoking) by 21 days being more likely to be abstinent at 6 months if they received longitudinal care rather than usual care. Similarly, those who did not respond to treatment by 3 months were more likely to be abstinent at 12 months if they received longitudinal care. At both time points, the likelihood of abstinence did not differ across treatment conditions among participants who responded to treatment (i.e., quit smoking). The effect on 6-month outcomes was mediated by satisfaction and readiness to quit. Cessation self-efficacy, satisfaction, and readiness to quit mediated the effect on 12-month outcomes. The effect of treatment condition on the likelihood of abstinence at 18 months was not moderated by response to treatment at 6 months. CONCLUSIONS: Smokers who did not respond to initial treatment benefited from longitudinal care. Differential effects of treatment condition were not observed among those who responded to early treatment. Conditional assignment to longitudinal care may be useful. Determining for whom and how interventions work over time will advance theory and practice.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".