An examination of cannabis-related information typically asked by consumers at retail cannabis locations: A Canadian survey of budtenders and managers
Bibliographic record
Abstract
Background: Since cannabis has been legalized in Canada for medicinal and recreational use, there has been an increased demand on pharmacists for cannabis counselling. The aim of the study was to examine typical questions posed by consumers to managers and budtenders working at licensed recreational cannabis stores in Canada and to assess how often consumers seek unlicensed medical advice to treat various conditions using cannabis. Methods: An online survey was synthesized, consisting of 22 questions capturing demographics and Likert scale responses to survey questions, and was distributed online across Canada from January to June 2021. Results: = 185) of respondents indicated that they receive questions related to cannabis use for medical purposes and/or perceived medical benefit, with the same number indicating that they have been told by a customer that their physician sent them to obtain a cannabis-containing product for medical purposes. The most common cannabis component asked about in an average day was THC (42% of responses). Conclusion: An alarming proportion of budtenders and managers in Canada report that they are fielding medical cannabis questions. This situation has the potential to put individuals at risk for drug-drug interactions and drug-disease interactions and to increase unnecessary hospitalizations due to adverse effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".