Alcohol and tobacco cue effects on craving in non-daily smokers.
Bibliographic record
Abstract
Non-daily smokers commonly smoke cigarettes following the consumption of alcohol, yet the reason(s) for this remains poorly understood. The present study examined the impact of alcohol consumption on responses in tobacco salient cues 49 male and 50 female non-daily smokers. After the administration of an alcohol, placebo, or control beverage, participants were exposed to series neutral video clips and tobacco smoking salient video clips, and their subjective states and heart rates were monitored. The timing of the exposure to the tobacco smoking clips was randomly determined to coincide with the timing of either the ascending limb or the descending limb of the blood alcohol concentration (BAC) curve of the alcohol beverage condition. The tobacco smoking clips were found to increase cigarette craving regardless of beverage condition or timing of exposure (p = .002). Alcohol consumption was associated with increased ratings of intoxication (p < .001), increased heart rate across participants (p < .001), and increased cigarette craving in female participants specifically (p = .017). Alcohol did not influence responses to the smoking videos. These results suggest that smoking salient cues and alcohol may impact cigarette craving in non-daily smokers through independent processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".