Clinician Beliefs and Practices Related to Cannabis
Bibliographic record
Abstract
Introduction: Medical cannabis (marijuana) use is legal in 33 U.S. states and the District of Columbia. Clinicians can play an important role in helping patients access and weigh potential benefits and risks of medicinal cannabis. Accordingly, this study aimed to assess clinician beliefs and practices related to cannabis. Methods: Data are from 1506 family practice doctors, internists, nurse practitioners, and oncologists who responded to the 2018 DocStyles, a web-based panel survey of clinicians. Questions assessed medicinal uses for and practices related to cannabis and assessed clinicians' knowledge of cannabis legality in their state. Logistic regression was used to assess multivariable correlates of asking about, assessing, and recommending cannabis. Results: Over two-thirds (68.9%) of clinicians surveyed believe that cannabis has medicinal uses and just over a quarter (26.6%) had ever recommended cannabis to a patient. Clinicians who believed cannabis had medicinal uses had 5.9 times the adjusted odds (95% confidence interval 3.9–8.9) of recommending cannabis to patients. Beliefs about conditions for medical cannabis use did not necessarily align with the current scientific evidence. Nearly two-thirds (60.0%) of clinicians surveyed incorrectly reported the legal status of cannabis in their state. Discussion: Findings suggest that while clinicians believe that cannabis has medicinal uses, they may not have a full understanding of the scientific evidence and may not accurately understand their state-based policies for cannabis legalization and use. Given that clinicians are responsible for recommending medicinal cannabis in most states that have legalized it, ongoing education about the health effects of cannabis is warranted.
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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 | 0.003 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".