Prospective Associations of Behavioral Economic Demand for Cannabis and Alcohol With Simultaneous Cannabis and Alcohol Use Among Young Adults
Bibliographic record
Abstract
Behavioral economic demand for cannabis and alcohol is robustly associated with cannabis use and alcohol use, respectively. However, few studies have examined the contributions of cannabis and alcohol demand to simultaneous cannabis and alcohol use, which is common among young adults. We examined prospective associations of cannabis demand and alcohol demand with propensity for simultaneous use (broadly defined as using both cannabis and alcohol in the same day) and with cannabis and alcohol consumption during simultaneous use days among young adults. Young adults reporting simultaneous use (N = 107) completed a Marijuana Purchase Task assessing cannabis demand and an Alcohol Purchase Task assessing alcohol demand. They then completed daily smartphone surveys over 21 days assessing cannabis and alcohol use. Multilevel models revealed that higher cannabis demand (i.e., higher Omax, Pmax, and intensity; lower elasticity) was uniquely associated with greater propensity for simultaneous use relative to nonuse. In addition, higher alcohol demand (lower elasticity) was uniquely associated with greater propensity for simultaneous use relative to cannabis-only use, and higher cannabis demand (higher break point and intensity, lower elasticity) was uniquely associated with greater propensity for simultaneous use relative to alcohol-only use. Furthermore, in models limited to simultaneous use days, greater cannabis demand (higher Omax, lower elasticity) and lower alcohol demand (higher elasticity) were uniquely associated with greater overall cannabis flower consumption, and higher alcohol demand (higher Omax, lower elasticity) was uniquely associated with greater overall alcohol consumption. Results suggest that individual differences in cannabis and alcohol demand may contribute to simultaneous cannabis and alcohol use behaviors in a substance-specific pattern. Furthermore, cannabis demand may more strongly drive the tendency to engage in simultaneous use (vs. nonuse) relative to alcohol demand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".