Medical marijuana programs — Why might they matter for public health and why should we better understand their impacts?
Bibliographic record
Abstract
OBJECTIVE: Although cannabis is an illegal drug, 'medical marijuana programs' (MMPs) have proliferated (e.g., in Canada and several US states), allowing for legal cannabis use for therapeutic purposes. While both health risks and potential therapeutic benefits for cannabis use have been documented, potential public health impacts of MMPs - also vis-à-vis other psychoactive substance use - remain under-explored. METHODS: We briefly reviewed the emerging evidence on MMP participants' health status, and specifically other psychoactive substance use behaviors and outcomes. RESULTS: While data are limited in amount and quality, MMP participants report improvements in overall health status, and specifically reductions in levels of risky alcohol, prescription drug and - to some extent - tobacco or other illicit drug use; at the same time, increases in cannabis use and risk/problem patterns may occur. CONCLUSION: MMP participation may positively impact - for example, by way of possible 'substitution effects' from cannabis use - other psychoactive substance use and risk patterns at a scale relevant for public health, also influenced by the increasing population coverage of MMPs. Yet, net overall MMP-related population health effects need to be more rigorously and comprehensively assessed, including potential increases in cannabis use related risks and harms.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".