Evaluating cannabis substitution for alcohol within the context of a Canadian managed alcohol program
Bibliographic record
Abstract
INTRODUCTION: Managed Alcohol Programs (MAPs) provide beverage alcohol alongside housing and social supports to mitigate alcohol-related harms among individuals experiencing severe alcohol use disorder (AUD) and unstable housing. MAPs have been shown to stabilize alcohol use, reduce alcohol-related harms, improve quality of life, and decrease emergency service utilization. However, concerns about the long-term health risks associated with high levels of alcohol use have driven interest in cannabis substitution as an additional harm reduction strategy. Given the lower harm profile of cannabis, its integration into MAPs offers a promising avenue for further reducing alcohol-related harms. This study evaluates a novel cannabis substitution program within a Canadian MAP, leveraging the unique context of cannabis legalization and harm reduction programming. METHODS: Beginning in January 2023, participants (N = 35) were offered the choice of a pre-rolled cannabis joint or their prescribed alcohol dose multiple times per day. Data were drawn from five waves of quantitative surveys (January 2023 to February 2024; n = 20), two years of program records (January 2022 to February 2024; N = 35), and qualitative interviews (n = 14). Hierarchical mixed-effects models were used to predict alcohol use by cannabis use and time. Qualitative data were analyzed using interpretive description methodology. RESULTS: The final model found evidence of a substitution effect: participants who used more cannabis on average also consumed less alcohol overall. Specifically, each additional 0.4-gram joint consumed (approximately 15.2 standard THC units or 76 mg THC) was associated with an estimated 2.43 fewer mean daily standard drinks. Within-person cannabis use was not a significant predictor, indicating that short-term fluctuations in cannabis use were not associated with concurrent changes in alcohol consumption. Alcohol use also declined over time. Qualitative findings provide insights into the dynamic factors shaping drinking and cannabis use patterns. CONCLUSION: This study highlights the potential for cannabis substitution to meaningfully reduce alcohol-related harms. Implications for program development and future research evaluating changes in health, wellbeing, and harm outcomes are discussed.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".