Too young for Cannabis? Choice of minimum legal age for legalized non-medical Cannabis in Canada
Bibliographic record
Abstract
BACKGROUND: Choice of minimum legal age (MLA) for cannabis use is a critical and contentious issue in legalization of non-medical cannabis. In Canada where non-medical cannabis was recently legalized in October 2018, the federal government recommended age 18, the medical community argued for 21 or even 25, while public consultations led most Canadian provinces to adopt age 19. However, no research has compared later life outcomes of first using cannabis at these different ages to assess their merits as MLAs. METHODS: We used doubly robust regression techniques and data from nationally representative Canadian surveys to compare educational attainment, cigarette smoking, self-reported general and mental health associated with different ages of first cannabis use. RESULTS: We found different MLAs for different outcomes: 21 for educational attainment, 19 for cigarette smoking and mental health and 18 for general health. Assuming equal weight for these individual outcomes, the 'overall' MLA for cannabis use was estimated to be 19 years. Our results were robust to various robustness checks. CONCLUSION: Our study indicated that there is merit in setting 19 years as MLA for non-medical cannabis.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".