Young adult concurrent use and simultaneous use of alcohol and marijuana: A cross-national examination among college students in seven countries
Bibliographic record
Abstract
INTRODUCTION: Many young adults report frequent co-use of alcohol and marijuana, with some individuals engaging in simultaneous use (SAM; use of both substances within the same occasion resulting in an overlap of their effects) and others in concurrent use (CAM; use of both substances during a similar time period [e.g., past 30 days] but not within the same occasion). Emerging work demonstrates that SAM relative to CAM use places individuals at a greater risk for substance-related harms; however, these results primarily rely on U.S. samples. The goal of the present multi-country study was to examine prevalence rates of CAM and SAM use and examine differences in past 30-day SAM/CAM use on alcohol/marijuana substance-related outcomes among college students from seven countries. METHODS: = 3.96) college students participated in the cross-sectional online survey study. RESULTS: Among students who endorsed use of both alcohol and marijuana in the past 30-days (n = 2124), SAM use (75.8%) was far more prevalent than CAM use (24.2%). Moreover, ∼75% of students endorsed SAM use within each country subsample. Regression models showed that SAM vs. CAM use was associated with greater alcohol and marijuana use and negative consequences. CONCLUSIONS: College students from around the world endorse high rates of SAM use, and this pattern of co-use is associated with greater frequency of use and substance-related harms. On college campuses, SAM use should be a target of clinical prevention/intervention efforts and the mechanisms underpinning the unique harms of SAM need to be clarified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".