Facilitators and barriers to the regulation of medical cannabis: a scoping review of the peer-reviewed literature
Bibliographic record
Abstract
BACKGROUND: In recent decades, several political, legislative, judicial, consumer, and commercial processes around the world have advanced legalization efforts for the use of medical cannabis (MC). As the use of MC 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 MC regulations. MEDLINE, EMBASE, AMED and PsycINFO databases were systematically searched; no restrictions were placed on geographic location/jurisdiction. Eligible articles included those that evaluated the MC regulatory framework of one or more countries. 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: Policymakers should be aware of facilitators to the MC regulation implementation process, such as the influence of state and federal congruence, increased transparency, and the incorporation of stakeholder concerns, in order to effectively respond to a growing societal acceptance of MC and its use among patients. Given a comprehensive understanding of these influential factors, policymakers may be better equipped to meet the consumer and commercial demands of a rapidly evolving MC regulatory environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".