Impacto de la legalización del consumo recreativo del cannabis
Bibliographic record
Abstract
In recent years, there have been important legislative changes in many countries regarding the use of cannabis for medicinal and/or recreational purposes, which have facilitated access to it. Uruguay, Canada and some of the US states are the only jurisdictions that have legalised recreational consumption, applying different legislative models. The aim of this review is to analyse the effects that the legalisation of recreational cannabis has had on its use and its consequences. In general, the evidence accumulated to date indicates that the legalisation of cannabis has been associated with a decrease in the price of the substance, higher concentration of THC (potency), greater diversity of presentations for consumption, lower risk perception and an increase in consumption in adults and moderately in adolescents (even though it is illegal for them to consume), as well as an increase in the adverse consequences derived from cannabis consumption on public health. There has been a decrease in drug-related arrests, but the illegal market continues to be frequently used. No increase in the demand for treatment due to cannabis consumption has been detected. Therefore, these legislative changes have so far failed to achieve their main objectives, which were to suppress the illegal market and protect the most vulnerable groups, while on the contrary, they seem to imply an increase in some of the negative aspects associated with cannabis consumption. However, taking into account that most of these legislative changes have entered into force relatively recently, a longer follow-up period is required to be able to draw definitive conclusions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".