Knowledge of Cannabinoids among Patients, Physicians, and Pharmacists
Bibliographic record
Abstract
Objective: Many patients hold false beliefs about cannabinoids.We evaluated their related beliefs and we also surveyed physicians and pharmacists about their opinions regarding cannabinoids. Materials and MethodStudy 1: 42 patients (mean age 39.1 years, SD=12.6,range 18 to 67) in urban methadone/suboxone clinics were surveyed via questionnaire about their use of cannabis and their knowledge of its potential medical applications and of its positive and negative properties.Study 2: We recruited 53 professionals (37 physicians and 16 pharmacists) to compare the utility and adverse side-effects of cannabinoids to those of other frequent non-opioid medications for pain, epilepsy, insomnia, and for loss of appetite in HIV positive patients. Results (both studies):Two-thirds (66.7%) of our patients reported using cannabis (71.4% of users via smoking, 46.4% in food, 28.6% as drops).The users knew significantly more (t=2.1,df=39, p=.043) legitimate medical applications of cannabis (mean=4.7,SD=2.9) than non-users (mean=2.1,SD=1.7).Most frequently listed medical applications were epilepsy (73.2%), cancer (70.7%), pain (65.9%), and arthritis (53.7%).However, only 52.4% of patients correctly attributed "drug induced psychosis" to tetrahydrocannabinol rather than to other cannabis constituents.Some erroneously attributed their "high" to cannabidiol (14.3%).The MDs and pharmacists who volunteered for our survey rated cannabinoids as being more free of adverse side-effects than some other commonly prescribed non-opioid medications for pain, insomnia, and for loss of appetite in HIV patients.Their ratings of cannabinoids for epilepsy were also relatively favourable.Conclusions: Patients need expert therapeutic guidance from their physicians and pharmacists to properly benefit from cannabinoids.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".