Editorial: Community series - Purple Haze: issues on cannabis legalization, volume II
Bibliographic record
Abstract
Considering the progressive legalization of cannabis across jurisdictions, we prepared a special topic that addresses significant issues relevant for future legalization initiatives. This special topic follows a first special topic on the same theme [HYPERLINK]. The current special topic seeks to: (i) document the psychiatric and cognitive consequences of cannabis products, used either for recreational or medical purposes; (ii) document the impacts of cannabis legalization in North America, with special attention to youth, emergency department visits and sex/gender differences;(iii) provide a framework for medical cannabis administration; and (iv) define priority areas deserving more research.Among the potential harms of cannabis, the association with psychosis is one of the issues that has received the most attention in the context of recreational cannabis legalization. As [HALL] points out, the nature of this is too often judged by the authors' preconceptions about merits of recreational marijuana with opponents of RML being prone to assert the causal nature of the association, supporters being to deny it. In his [HALL]shows that the literature is sufficiently robust to support a interpretation of the association 50 between cannabis and psychosis. With nuance, he argues that one can nonetheless be in favor of 51 RML, as taking a stance on such a policy requires considering all its advantages and disadvantages, 52 and that presumed advantages (example: reducing the share of the illegal market, increasing 53 potential access to prevention) may outweigh its disadvantages.In addition to psychosis, the impact of cannabis on youth is another major concern. In their scoping In the Canadian context, [RUBIN-KAHANA and colleagues] examined the impact of RML on 75 youth. To date, the data do not suggest a marked increase in consumption among young people.On the other hand, preliminary results suggest a potential increase in hospitalizations and 77 emergency department (ED) visits among young people, but these trends remain to be confirmed.In the future, research will need to pay close attention to high-potency cannabis use among young In an article devoted to cannabis-related ED visits, [CROCKER and colleagues] observed an 89 increase in ED visits following the legalization of recreational cannabis use. Although these events 90 are not frequent, cannabis-related ED presentations are complex; hence, it would be relevant to 91 make brief interventions available to those affected. Furthermore, with the increase in delta-9-tetrahydrocannabinol 9 -THC) content in cannabis, the authors mention that it is reasonable to 93 anticipate that the prevalence of cannabis-related ED visits the future.The understanding of the impacts of RML extend beyond The unfolding of changes 96 in policy also critically matter. In Canada, province Ontario had the highest number of in-97 person retail stores 3 years after in October 2018. Using data from the Ontario Cannabis 98 Store, [TASSONE and colleagues] draw a detailed portrait of the products sold in this province.Beyond the diversity of products, what is particularly striking is that most inhaled products have 100 concentrations of ∆9-THC higher than 20%. There is growing evidence that high-potency cannabis 101 produces more harm. 1 However, this literature is based on a cut-off of 10% to classify cannabis as 102 having high-potency. These data illustrate the urgent need to update our knowledge on cannabis 103 with a potency higher than 20%.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.043 | 0.027 |
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".