Sources of Cannabis Information and Medical Guidance for Neurologic Use
Bibliographic record
Abstract
Background and Objectives: As cannabis products become increasingly accessible across the United States, understanding how patients obtain medical information on cannabis and view the role of their health care provider in providing information is important. Methods: -tetrahydrocannabinol-containing cannabis use between March and April 2020. Participants reported dialogue with health care providers regarding cannabis use, information sources used to make product decisions, and expenditure on cannabis. Findings are reported using descriptive statistics. Results: Overall, 3,249 participants responded (47% response rate), of whom 31% ever used cannabis and 20% currently used cannabis for MS. To determine presumed cannabis contents, respondents who had ever used cannabis (ever users) most often used dispensary-provided information (39%), word of mouth/dealer/friend (29%), and unregulated product labels (24%). For general information on cannabis for MS, ever users most often used dispensary staff (38%) and friends (32%). The primary source of medical guidance among ever users was most often "nobody or myself" (48%), followed by a dispensary professional (21%); only 12% relied on their MS physician, although 70% had discussed cannabis with their MS physician. Most current users (62%) typically sourced their cannabis from a dispensary. The most common factor in selecting a cannabis product was perceived quality and safety (70%). Discussion: Participants most often received information on cannabis for MS from dispensaries, unregulated product labels, and friends; only a small proportion used health care providers. Evidence-based patient and physician education is needed.
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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 | 0.002 | 0.029 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".