Home cultivation across Canadian provinces after cannabis legalization
Bibliographic record
Abstract
Aims: Little research exists on home cultivation in Canada after non-medical cannabis legalization in 2018. The aims of the study were to: (1) estimate the percentage of home cultivation before and after legalization; (2) estimate the quantity and expenditure of cannabis plants; and (3) examine the association between provincial policies and home cultivation after legalization. Methods: Repeat cross-sectional survey data come from Canadian respondents in the International Cannabis Policy Study in 2018, 2019, and 2020. Respondents aged 16-65 were recruited through online commercial panels. Home cultivation rates were estimated among all respondents in 2019 and 2020 (n = 26,304) and among a sub-sample of past 12-month cannabis consumers in 2018-2020 (n = 12,493). Weighted multivariable logistic regression models examined the association between home cultivation and provincial policies among all respondents, 2019-2020. Results: Cannabis consumers in 2019 (7.9%; AOR = 1.47, 95% CI: 1.07,2.01) and 2020 (8.8%; AOR = 1.62, 95 %CI: 1.18,2.23) had higher odds of reporting home cultivation in the past 12 months than pre-legalization (5.8%). Post-legalization, past 12-month home cultivation was lower in Quebec and Manitoba, the two provinces that prohibited home cultivation (3.2%), than in provinces where home cultivation was permitted (6.8%; AOR = 0.48, 95 %CI: 0.39, 0.59). The median number of plants grown across all provinces was between 3.1 and 3.5 in all years. Conclusions: Almost one in ten Canadian cannabis consumers reported home cultivation of cannabis in 2020, with modest increases following legalization and most growing within the non-medical limit of four plants. Home cultivation was less common in provinces where home cultivation was prohibited.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".