Self-control depletion and nicotine deprivation as precipitants of smoking cessation failure: A human laboratory model.
Bibliographic record
Abstract
OBJECTIVE: The need to understand potential precipitants of smoking relapse is exemplified by relapse rates as high as 95%. The Self-Control Strength model, which proposes that self-control is dependent upon limited resources and susceptible to fatigue, may offer insight into relapse processes. The current study tested the hypothesis that self-control depletion (SCD), produced from engagement in emotional suppression, would serve as a novel antecedent for cessation failure, as indexed by a validated laboratory analogue of smoking lapse and relapse. We also examined whether SCD effects interacted with those of a well-established relapse precipitant (i.e., nicotine deprivation). Craving and behavioral economic indices (delay discounting and demand) were tested as hypothesized mechanisms for increased cessation failure. Ultimately, a moderated mediation model was used to test nicotine deprivation as a hypothesized moderator of SCD effects. METHOD: We used a 2 × 2 (12-hr deprivation vs. no deprivation; SCD vs. no SCD) factorial between-subjects design (N = 128 smokers). RESULTS: The primary hypothesis of the study was supported, as SCD increased lapse behavior (p = .04). Nicotine deprivation significantly increased craving, cigarette demand, delay discounting, and lapse behavior. No main effects were found for SCD on putative mediators (i.e., craving, demand, and discounting), but the SCD and deprivation manipulations interacted upon craving (p = .04). The moderated mediation model was significant. SCD was found to increase craving among nicotine deprived smokers, which mediated effects on lapse behavior. CONCLUSIONS: SCD appears to play an important role in smoking relapse and may be a viable target for intervention. (PsycINFO Database Record
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".