Global Impacts of Legalization and Decriminalization of Marijuana and Cannabis
Bibliographic record
Abstract
ABSTRACT Purpose: The purpose of this article is to discuss global public health impacts of legalization and decriminalization of marijuana and cannabis regarding physical and mental health impacts as well as accident and death reports. Method: Extensive review of the medical literature was performed with a focus on papers discussing medical effects from marijuana use, public health impacts from legalization and decriminalization of marijuana and accident and death reports in various countries after legalization and decriminalization of marijuana. PubMed and Medline were utilized in this search. A total of nineteen articles have been referenced in this project. Most of the information was obtained from studies performed in the United States, Canada, Australia and Uruguay. Key words: marijuana, THC, cannabinoids, legalization, decriminalization, mental health, psychosis, accidents, deaths, lung disease Results: Articles were obtained from various countries regarding public health impacts of marijuana and cannabis use as well as an increase in adverse events after legalization and decriminalization of marijuana and cannabis. Information was available regarding cardiopulmonary diseases and mental health impacts from marijuana and cannabis use. Given that many countries have not yet legalized or decriminalized marijuana there were some limitations in the available information. Several countries reported an increase in motor vehicle accidents and deaths after legalization or decriminalization of marijuana. Marijuana users were noted to have an increase in negative mental health, including psychosis and suicidal behaviors, when compared to nonusers. Data demonstrates an increase in marijuana use after legalization or decriminalization.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".