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
OBJECTIVE: People who use cannabis for medicinal (vs. nonmedicinal) reasons report greater cannabis use and lower alcohol use, which may reflect a cannabis-alcohol substitution effect in this population. However, it is unclear whether cannabis is used as a substitute or complement to alcohol at the day level among people who use cannabis for both medicinal and nonmedicinal reasons. This study used ecological momentary assessment to examine this question. METHOD: Participants (N = 66; 53.1% men; mean age 33 years) completed daily surveys assessing previous-day reasons for cannabis use (medicinal vs. nonmedicinal), cannabis consumption (both number of different types of cannabis used and grams of cannabis flower used), and number of standard drinks consumed. RESULTS: Multilevel models revealed that, in general, greater cannabis consumption on a given day was associated with greater same-day alcohol use. Further, days during which cannabis was used for medicinal (vs. exclusively nonmedicinal) reasons were associated with reduced consumption of both cannabis and alcohol. The day-level association between medicinal reasons for cannabis use and lower alcohol consumption was mediated by using fewer grams of cannabis on medicinal cannabis use days. CONCLUSIONS: Day-level cannabis-alcohol associations may be complementary rather than substitutive among people who use cannabis for both medicinal and nonmedicinal reasons, and lower (rather than greater) cannabis consumption on medicinal use days may explain the link between medicinal reasons for cannabis use and reduced alcohol use. Still, these individuals may use greater amounts of both cannabis and alcohol when using cannabis for exclusively 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".