Does Cannabis Use Predict More Severe Types of Alcohol Consequences? Longitudinal Associations in a 3‐Year Study of College Students
Bibliographic record
Abstract
BACKGROUND: Prior research shows that negative drinking outcomes among young adults may be exacerbated by cannabis use. However, to date, there have been few longitudinal studies of associations between cannabis use and negative alcohol-related consequences. This study examined longitudinal within-person associations between cannabis use and several domains of negative alcohol consequences among young adults and explored the moderating role of sex. METHOD: We analyzed data from N = 997 students assessed 4 times per year over the first 3 years of university. At each time point, participants completed measures of past-month cannabis use frequency, typical weekly number of drinks, and 8 domains of negative alcohol consequences. Longitudinal associations were examined in multilevel models. RESULTS: Within-person changes in frequency of cannabis use were not uniquely associated with changes in total alcohol consequences aggregated across several alcohol consequence domains. However, when examining alcohol consequence domains separately, within-person increases in cannabis use frequency were specifically associated with increases in some (but not all) of the more severe types of alcohol consequences, including risky behaviors, poor self-care, and alcohol dependence symptoms. No support was observed for the moderating role of sex in the longitudinal within-person associations between cannabis use and alcohol consequence domains. CONCLUSIONS: Findings suggest that within-person changes in cannabis use frequency among young adults are associated with corresponding changes in some domains of alcohol consequences (but not others) when examined over the course of several years. Results may inform targeted harm reduction interventions for young adult drinkers who use cannabis, although future research is needed to clarify the mechanisms of the observed associations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".