Cannabidiol as a Harm Reduction Strategy for People Who Use Drugs: A Rapid Review
Bibliographic record
Abstract
OBJECTIVE: The drug poisoning crisis throughout North America necessitates novel harm reduction approaches. Emerging evidence suggests that cannabidiol (CBD) may have some utility as a harm reduction modality for those with problematic substance use. This rapid review aimed to synthesize available evidence on CBD as a potential harm reduction tool for people who use drugs while providing clinical and research insights. METHOD: A systematic search in EMBASE, MEDLINE, CENTRAL, and CINAHL was completed in July 2022. For inclusion, studies had to meet the following criteria: (1) drawn from an adult population of people who use drugs; (2) investigates CBD as an intervention for problematic substance use or harm reduction-related outcomes; (3) be published after the year 2000 and in English; and (4) be primary research or a review article. A narrative synthesis was used to group outcomes relevant to harm reduction and provide clinical and research insights. RESULTS: We screened 3,134 records, of which 27 studies (5 randomized trials) were included. The evidence remains limited, but available studies support the potential utility of CBD to reduce drug-induced craving and anxiety in opioid use disorder. There were low-quality studies suggesting that CBD may improve mood and general well-being of people who use drugs. Evidence suggests that CBD monotherapy may not be an adequate harm reduction strategy for problematic substance use but rather an adjunct to the standard of care. CONCLUSION: Low-quality evidence suggests that CBD may reduce drug cravings and other addiction-related symptoms and that CBD may have utility as an adjunct harm reduction strategy for people who use drugs. However, there is a significant need for more research that accurately reflects CBD dosing and administration regimens used in a real-world context.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".