Highs and Lows: A Mixed-Methods Analysis of the Impact of Adult Use Legalization on Medical Cannabis Patients
Bibliographic record
Abstract
Presently, 24 states have legalized adult use (recreational) cannabis, each following medical-only access. Although states that pass adult use laws report substantial declines in the number of registered medical patients, these laws expand the market, potentially enhancing patient benefits. However, research on federal adult use cannabis legislation in Canada suggests that adult use laws negatively impact medical patients. The purpose of this mixed-methods study was to examine medical cannabis patients' perceptions of the impact of adult use cannabis laws in the US. We conducted an online survey with forced choice and open-ended questions in a convenience sample of 505 medical cannabis patients. Quantitative analysis indicated that adult use laws decreased stress and legal concerns, and that patient perceptions of cannabis product quality and availability were higher, but prices were also higher. Qualitative analysis largely aligned with quantitative findings, however data were somewhat divergent on price and product availability (with some patients reporting lower prices and accessibility issues). Mixed-methods analysis revealed that legalization also reduced patients' experience of social stigma. Addressing the patient concerns revealed by these data may help to restore or maintain patient access to affordable, medically relevant cannabis products as additional states merge medical cannabis programs into adult use paradigms.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".