Impact of the COVID-19 pandemic on cannabis cultivation and use in 18 countries
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and the accompanying measures to mitigate infection affected many areas of society, including the supply and use of cannabis. This paper explored how patterns of behaviour among people who cultivate cannabis were affected by the COVID-19 pandemic and restrictions. METHODS: An anonymous web survey of people who cultivated cannabis was conducted from Aug 2020 to Sep 2021, spanning 18 countries and 11 languages (N = 11,479). Descriptive statistics and mean comparison tests were conducted. RESULTS: Most cannabis growers reported that their practices were relatively unaffected by the COVID-related restrictions. While 35.2 % reported difficulties buying cannabis from their usual dealer, <10 % stated that access to materials needed for growing was impaired during the pandemic. Over one-quarter (28.2 %) of respondents increased their cannabis use and 21.4 % also increased cannabis cultivation (more than twice as many as those who said they were growing less or not anymore) while COVID restrictions were in place. People who lost their job or were casually employed were more likely to increase use and cultivation. Overall, the pandemic had little impact on reasons for growing, however, difficulties obtaining cannabis were mentioned as the most prevalent COVID-19-related growing motive. A small number (16 %) reported starting their growing activity during the pandemic. Italian and Portuguese growers were more likely to report shortages in supply and increases in their growing activity. CONCLUSIONS: This study is the first to document an increase in cannabis cultivation activity following COVID restrictions. Increased home cultivation was not only driven by higher use as a result of home isolation, but also by disruptions of wider illegal cannabis supply. Limitations of this study include the non-representativeness of the sample as well as differences in approaches and duration of restrictions in different countries.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".