Home cannabis cultivation in the United States and differences by state-level policy, 2019-2020
Bibliographic record
Abstract
Background: As of 2022, all but two U.S. states with adult-use cannabis laws also allow home cultivation. Home cultivation has the potential to support or oppose public health measures, and research in U.S. states is nascent. Objectives: 1) estimate the percentage of respondents who reported growing cannabis plants; 2) estimate the average number of plants grown; 3) examine the association between home cultivation, jurisdiction, and individual-level factors; and 4) examine the association between home cultivation and state-level policies in adult-use states. Methods: Repeat cross-sectional survey data come from U.S. respondents aged 21–65 in 2019 and 2020. Respondents were recruited through online commercial panels. Home cultivation rates were estimated among all U.S. respondents (n = 51,503; 46–52% male). Additional analyses were conducted on a sub-sample of respondents in states that allowed adult-use home cultivation (n = 29,100; 50% male). Results: A total of 6.8% and 7.3% of U.S. respondents reported home cultivation in 2019 and 2020, respectively. Respondents in states that allowed adult-use home cultivation had higher odds of reporting home cultivation than respondents in states without medical or adult-use cannabis laws (AOR = 1.48, 95% 1.26, 1.75). Among respondents in states that allowed adult-use home cultivation, the median number of plants that respondents reported growing was below state cultivation limits. Conclusion: Home cultivation rates in the U.S. were higher in states that allowed adult-use home cultivation; however, other evidence suggests these same states had higher rates predating adult-use legalization. Further work is needed to examine how home cultivation relates to public health measures in adult-use states.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".