Descriptive norms for simultaneous cannabis and alcohol use predict simultaneous use patterns assessed via daily surveys.
Bibliographic record
Abstract
OBJECTIVE: Simultaneous cannabis and alcohol use is common, but few studies have examined normative perceptions of simultaneous use. This study examined unique associations of baseline descriptive norms for simultaneous use (i.e., perceptions about others' simultaneous use) with simultaneous use behaviors assessed via daily surveys. METHOD: = 150) completed baseline measures of descriptive norms for the frequency of simultaneous use and the amounts of cannabis and alcohol consumed during typical simultaneous use occasions. Further, participants completed measures of descriptive norms for the frequency and quantity of cannabis and alcohol use in general (not limited to simultaneous use). Norms were assessed referencing both peer and friend groups. Following this assessment, participants completed 21 daily smartphone surveys assessing cannabis and alcohol use each day. Simultaneous use was operationalized as same-day use of cannabis and alcohol. RESULTS: Multilevel models revealed that, controlling for descriptive norms for cannabis and alcohol use in general, perceiving more frequent simultaneous use among friends (but not peers) was significantly associated with a greater tendency to engage in simultaneous use relative to cannabis-only use across days. Further, perceiving heavier cannabis and alcohol consumption during simultaneous use occasions among friends (but not peers) was significantly associated with greater quantities of cannabis and alcohol consumed, respectively, across simultaneous use days. CONCLUSIONS: Descriptive norms for simultaneous use contribute uniquely to simultaneous use behavior, over and above norms for cannabis use and alcohol use in general. Findings may inform norms-based interventions for young adults who engage in simultaneous use. (PsycInfo Database Record (c) 2025 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".