Dispensing Medical Advice: San Francisco Bay Area Budtender Recommendations for Pain and Sleep Relief
Bibliographic record
Abstract
Objective: Evidence regarding the efficacy of various forms of cannabis and cannabinoid concentrations is limited, and cannabis industry regulatory infrastructure is still in development. Meanwhile, most US states have legalized medical or adult use cannabis. This study aimed to understand what advice cannabis budtenders in the San Francisco Bay Area were providing to customers for pain and sleep trouble - two of the conditions most cited as reasons for using cannabis medicinally. Method: We visited 35 of 42 cannabis dispensaries in Alameda and San Francisco Counties in California, and using a "secret shopper" approach, asked the budtenders for recommendations on products, dosage, and strains to best alleviate pain and sleep trouble. Results: For pain relief, budtenders showed a strong preference for topicals (77.1%), while edibles were most indicated for sleep trouble (60.0%). Reasons provided included budtender personal experience and product effectiveness. Cannabidiol (CBD) was endorsed most often for pain relief in high CBD:THC ratios (28.6%), 1:1 ratios (28.6%), and CBD alone (22.9%). For sleep relief, tetrahydrocannabidiol (THC) alone was most recommended (34.3%). When asked about cannabis strains for pain, 85.7% of budtenders did not express a preference, but for sleep, 57.1% of budtenders selected indica. Conclusions: This study illustrates that budtenders in the Bay Area have specific ideas about cannabis uses, including types, concentrations, and strains, despite a lack of evidence for most recommendations. Future research should prioritize study of topical preparations of cannabis for pain, edibles for sleep, and tinctures for both, which budtenders regularly recommended to customers.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".