Legalization of cannabis leads to increase in use, decrease in use disorder
Bibliographic record
Abstract
The frequency of cannabis use increased signifcicantly, but misuse discreased, in a study of almost 1,500 adults. A prospective cohort study has found that five years after legalization, the frequency of cannabis use increased modestly, while cannabis misuse decreased modestly. Frequent consumers of cannabis before legalization showed the largest decreases in both frequency of use and cannabis misuse after legalization. The study was conducted on community‐dwelling (living independently in a house or apartment, not an institution) adults assessed 11 times from September 2018 to October 2023 in Ontario. The results, according to study authors, suggest that recreational cannabis legalization was associated with modest negative and positive consequences. The study, “Cannabis Use and Misuse Following Recreational Cannabis Legalization,” is published in the April 23 issue of JAMA Network Open, and by André J. McDonald, PhD, MPH and colleagues. For the study, frequency was defined as the mean proportion of days using cannabis, which went up by 1.75% over 5 years. In cased, cannabis use disorder scores decreased significantly over 5 years. Interestingly, the decrease of cannabis use disorder was most pronounced with the onset of the COVID‐19 pandemic. Over that 5‐year period, product preferences shifted away from dried flower, hashish, concentrates, oil, tinctures, and topics to edibles, liquids, and vape pens.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".