Non-medical cannabis use: international policies and outcomes overview. An outline for Portugal
Bibliographic record
Abstract
INTRODUCTION: Cannabis is probably the most commonly used illicit drug. It is often regarded as a relatively nonharmful experience, even though evidence indicates otherwise. Legalization of non-medical cannabis, which has already taken place in several countries, is currently a controversial issue. OBJECTIVE: To provide an up-to-date overview of current models and policies and their outcomes that can inform future political decisions regarding non-medical cannabis use. METHODS: PubMed/MEDLINE and Google Scholar scientific databases were searched for articles written in English, Spanish, and Portuguese published between 1990 and December 2020. The reference lists of these articles were similarly used as bibliography sources. Gray literature was also included. RESULTS: While non-medical cannabis has been decriminalized in many countries, it has only been legalized in Uruguay, Canada, and some U.S. states. Several benefits of legalization were identified: decreases in cannabis-related crimes, law-enforcement and judicial costs; reduction in synthetic cannabis supply; decline in black economies and possible diminution of other illegal drug buying; and tax revenue increases. Reported legalization problems included: increases in cannabis use; cannabis-related disorders; and cannabis-related accidents and hospitalizations. Harm-reduction strategies are available in the scientific literature. CONCLUSION: Growing, although incomplete, evidence exists to guide policy makers, minimize cannabis-related harm, and positively contribute to public health, if the legalization path is to be followed. Dialogue between legislators and science should be encouraged. There are more than a few legalization pathways, with diverse economic, social and health wellbeing effects. Public health-driven, instead of profit-driven models, seem to offer the most benefits regarding non-medical cannabis legalization. Most of the true public health effects of cannabis legalization are still unknown, for we are still in the early stages of these policies and their implications. Future studies should address the medium-to-long-term social, economic, and health consequences of legalization policies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".