Evaluating the temporal relationships between withdrawal symptoms and smoking relapse.
Bibliographic record
Abstract
Smokers attempting to quit often attribute smoking relapse to negative affect, craving, and other nicotine withdrawal symptoms. In addition, there is evidence that smoking relapse can increase these symptoms, particularly negative affect. To address this issue, we analyzed data from an 11-week smoking cessation clinical trial in which smokers (n = 1,246) were randomized to receive either nicotine replacement therapy (NRT), varenicline, or placebo, combined with behavioral counseling. Using cross-lagged analyses, we examined the temporal bidirectional relationships between self-reported measures of affect, craving, and composite withdrawal symptoms and biochemically verified smoking abstinence. The relative strength of these temporal relationships was examined by comparing the explained variances of the models. The results showed that higher negative affect, craving, and composite withdrawal symptoms increased the likelihood of subsequent smoking relapse, and that smoking relapse led to subsequent increases in these same symptoms. A comparison of the explained variances found symptom predicting subsequent relapse models to be stronger than those where relapse predicted subsequent symptoms. Although the explained variance findings generally support a negative reinforcement conceptualization of nicotine dependence, the bidirectional relationship between symptoms and smoking relapse suggests that struggling with quitting smoking leads to significant negative affect, craving, and other withdrawal symptoms that do not quickly resolve. These findings highlight the importance of addressing specific symptoms within the context of smoking cessation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".