Examining Daily Associations Between Cannabis Use and Alcohol Use Among People Who Use Cannabis for Both Medicinal and Nonmedicinal Reasons: Substitution or Complementarity?
Bibliographic record
Abstract
People who use cannabis for medicinal reasons tend to report elevated cannabis use and reduced alcohol use, which may reflect a cannabis–alcohol substitution effect. However, it is currently unclear whether cannabis is used as a substitute for or complement to alcohol at the day level among individuals who use cannabis for both medicinal and nonmedicinal reasons. This study used ecological momentary assessment (EMA) to examine day-level cannabis-alcohol associations linked to day-level variation in medicinal (versus nonmedicinal) reasons for cannabis use. People reporting cannabis use for both medicinal and nonmedicinal reasons (N=66) completed daily surveys assessing previous-day reasons for cannabis use, cannabis consumption, and alcohol consumption. Multilevel models revealed that days during which cannabis was used for medicinal (versus exclusively nonmedicinal) reasons were associated with reduced consumption of both cannabis and alcohol, and alcohol use was increased on days involving greater cannabis consumption. Further, the day-level association between medicinal (versus exclusively nonmedicinal) reasons for cannabis use and lower alcohol consumption was mediated by fewer grams of cannabis used on those days. Results suggest that day-level cannabis-alcohol associations may be complementary rather than substitutive among people who use cannabis for both medicinal and nonmedicinal reasons, and reduced (rather than increased) cannabis use may explain the link between medicinal reasons for cannabis use and reduced alcohol use. These individuals may still be at risk for cannabis-alcohol co-use-related harms, especially on days when they use cannabis for nonmedicinal reasons.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".