Behavioral Economic Demand for Alcohol and Cigarettes in Heavy Drinking Smokers: Evidence of Asymmetric Cross-commodity Reinforcing Value
Bibliographic record
Abstract
INTRODUCTION: Previous studies have highlighted a strong bidirectional relationship between cigarette and alcohol consumption. To advance our understanding of this relationship the present study uses a behavioral economic approach in a community sample (N = 383) of nontreatment seeking heavy drinking smokers. AIMS AND METHODS: The aims were to examine same-substance and cross-substance relationships between alcohol and cigarette use, and latent factors of demand. A community sample of nontreatment seeking heavy drinking smokers completed an in-person assessment battery including measures of alcohol and tobacco use as well as the Cigarette Purchase Task and the Alcohol Purchase Task. Latent factors of demand were derived from these hypothetical purchase tasks. RESULTS: Results revealed a positive correlation between paired alcohol and cigarette demand indices (eg, correlation between alcohol intensity and cigarette intensity) (rs = 0.18-0.46, p ≤ .003). Over and above alcohol factors, cigarette use variables (eg, Fagerström Test for Nicotine Dependence and cigarettes per smoking day) significantly predicted an additional 4.5% (p < .01) of the variance in Persistence values but not Amplitude values for alcohol. Over and above cigarette factors, alcohol use variables predicted cigarette Persistence values (ΔR2 = .013, p = .05), however, did not predict Amplitude values. CONCLUSIONS: These results advance our understanding of the overlap between cigarette and alcohol by demonstrating that involvement with one substance was associated with demand for the other substance. This asymmetric profile-from smoking to alcohol demand, but not vice versa-suggests that it is not simply tapping into a generally higher reward sensitivity and warrants further investigation. IMPLICATIONS: To our knowledge, no study to date has examined alcohol and cigarette demand, via hypothetical purchase tasks, in a clinical sample of heavy drinking smokers. This study demonstrates that behavioral economic indices may be sensitive to cross-substance relationships and specifically that such relationships are asymmetrically stronger for smoking variables affecting alcohol demand, not the other way around.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".