Medical Cannabis and Past-Year Cannabis Use Disorder Among Adult Recreational Users in the United States: Results From a Nationally Representative Sample
Bibliographic record
Abstract
Introduction: With the increasing number of cannabis users and more jurisdictions allowing medical cannabis, more evidence-based knowledge about the prevalence of cannabis use disorder (CUD) among medical users is greatly needed. Objectives: To examine and compare the prevalence and severity of CUD and the prevalence of different CUD criteria among two groups: those who combine recreational and medical use vs. those who exclusively use cannabis recreationally. To examine the association between CUD and sociodemographic characteristics, medical conditions, and psychiatric comorbidities between these two groups. Methods: The National Epidemiological Survey on Alcohol and Related Conditions III data were used, a US nationally representative in-person interview of 36,309 adults aged ≥18 years collected in 2012-2013. The statistical examination included proportion comparison hypothesis testing and linear regressions, all using complex survey design analysis procedures. Results: Recreational users who used cannabis also for medical purposes had a higher prevalence of CUD in general, as well as mild and moderate CUD than users who used cannabis only for recreational purposes. CUD is more prevalent in recreational, medical users with the following characteristics: young, male, non-white, living in the Midwest, using a greater amount of cannabis, having a concurrent mental disorder, and had CUD before the past year. Conclusion: Recreational, medical cannabis users have a higher likelihood of having CUD. Although the results should be taken with caution, given the lack of established validity of CUD among medical users, health care professionals who prescribe or recommend the use of cannabis for medical purposes should take this into consideration while evaluating the risks/benefits ratio of cannabis. They need to assess patients' recreational cannabis use, screen for CUD, and educate users about the possible complications caused by cannabis use.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".