Cannabis-alcohol co-use protects from drinking regret without increasing harms
Bibliographic record
Abstract
Heavy episodic drinking, defined as three drinks consumed on a single occasion in females and four drinks in males, is a prevalent and dangerous behaviour. It is becoming increasingly common for cannabis to be co-used with alcohol such that the psychoactive effects of the two substances overlap. Research has indicated that the association between cannabis-alcohol co-use and short-term harms may be complicated by third variable factors such as number of drinks, but further research is needed to understand these complexities. Additionally, little is known about how co-use relates to other short-term outcomes beyond harms such as injury or embarrassment. The present study sought to explore the mechanisms by which co-use relates to harm through indirect pathways of number of drinks and psychoactive effects. Furthermore, co-use was investigated in relation to next-day regret and achievement of intended outcomes of cannabis and alcohol use. Ecological momentary assessment methodology was used to collect observations longitudinally among young adults in Canada. At the end of a drinking event, participants completed a validated measure to assess alcohol related stimulation and sedation psychoactive effects. The following morning, participants reported on harms, co-use with cannabis, drinks, regret, and intention fulfilment. Co-use was found to be moderated by number of drinks such that co-use was increasingly protective of harms as additional drinks were consumed. Moreover, co-use was associated with significantly less regret relative to when alcohol was consumed without cannabis. Finally, among participants that reported motivation to engage in cannabis- or alcohol-use for the purposes of enhancement, coping, and socializing, co-use was found to better achieve these outcomes compared to alcohol-use alone. Together, these findings indicate that co-use is no more harmful than alcohol-use alone, and at extreme levels of drinking is associated with protective outcomes. Contemporary harm reduction guidelines are designed to discourage cannabis-alcohol co-use with the intention of reducing harm. Findings suggest that empirically informed approaches should focus reduction strategies on drinks, rather than co-use. Future research should aim to investigate potential protective effects of co-use to determine contexts where co-use may be harm-reducing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".