An Evaluation of Regulatory Regimes of Medical Cannabis: What Lessons Can Be Learned for the UK?
Bibliographic record
Abstract
This paper evaluates current regulatory regimes of medical cannabis using peer-reviewed and grey literature as well as personal communications. Despite the legalization of medical cannabis in the UK in November 2018, patients still lack access to the medicine, with fewer than 10 NHS prescriptions having been written to date. We look at six countries that have been at the forefront of prescribing medical cannabis, including case studies of the three largest medical cannabis markets in the EU: Germany, Italy, and the Netherlands. Canada, Israel and Australia add global examples. These countries have a more successful history of prescribing medical cannabis than the UK. Their legislations are outlined and numbers of medical cannabis prescriptions are provided to give an indication of how successful their regulatory regime has been in providing patient access. Evaluating countries' medical cannabis regulations allows us to offer implications for lessons to be learned for the development of a successful medical cannabis regime in the UK.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".