Co-Use of Alcohol and Cannabis: Longitudinal Associations with Mental Health Outcomes in Young Adulthood
Bibliographic record
Abstract
Increases in cannabis use among young people has heightened concern about the potential interactive health effects of cannabis with other drugs. We examined the longitudinal association between concurrent and simultaneous (SAM) co-use of alcohol and cannabis in young adulthood on mental health symptoms, substance use behaviors, and substance-related harms two years later. Data were drawn from Time 5 (T5; n = 464; 46% male) and 6 (T6; n = 478; 45% male) of the Victoria Healthy Youth Survey. At T5, 42% of participants used alcohol-only, 13% used concurrently, 41% used SAM, 1% were cannabis only users, and 3% abstained from cannabis and alcohol. Boys were more likely to use SAM. Higher T5 SAM use frequency was associated with heavier use of substances, more substance-related harms, and symptoms of psychosis and externalizing problems at T6. T5 Concurrent use was associated with conduct symptoms, illicit drug use, and alcohol use disorders at T6 relative to alcohol-only use. Cannabis is commonly used with alcohol and the findings suggest that any co-use (concurrent or simultaneous) may be problematic in young adulthood. Public health messages need to explicitly inform consumers about the possible consequences of using both alcohol and marijuana and the addictive pharmacological impact of using them together.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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".