Facilitators and Barriers to the Regulation of Medical Cannabis: A Scoping Review of the Peer-Reviewed Literature
Bibliographic record
Abstract
Abstract Background: In recent decades, several political, legislative, and judicial processes around the world have advanced legalization efforts for the use of medical cannabis (MC). As MC usage evolves through legislative reform, with an increase in public acceptance and therapeutic potential, a need exists to further investigate the facilitators and barriers to MC regulation.Methods: A scoping review was conducted to identify the facilitators and barriers associated with the implementation of international MC regulations. MEDLINE, EMBASE, AMED and PsycINFO databases were systematically searched. Eligible articles included primary studies that evaluated the MC regulatory framework of one or more countries globally.Results: Twenty-two articles were deemed eligible and included in this review. Themes identified include: (1) effects of conflicts, mindset and ideology of state population, (2) the use of comparisons to analyze MC regulation, and (3) the need for more knowledge, advice and empirical/clinical evidence to inform future MC policies.Conclusion: We identified a number of facilitators and barriers to MC regulation and provide a holistic overview of what factors are proposed to affect MC regulation. In recognizing that the evidence-base surrounding MC, MC usage among patients, and general societal acceptance of MC are all increasing across many parts of the globe, our review allows for relevant stakeholders to better understand these facilitators and barriers to inform future MC policy making.
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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 | 0.026 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".