A Quality Assessment of Information Provided on Websites Selling Cannabis to Consumers in Canada
Bibliographic record
Abstract
Abstract Background: Cannabis is used by millions of people for both medical and recreational purposes, and this use is even greater in jurisdictions where it is legalized, such as Canada. Online cannabis vendors have gained popularity for purchasing cannabis due to the easy access and convenience to consumers. The objective of this study was to evaluate the quality of health information provided by websites of cannabis vendors selling products to Canadian consumers, and to further identify trends in the information provided. Methods: Six different searches were conducted on Google.ca, and the first 40 webpages of each search were screened for eligibility. A total of 33 unique websites of cannabis vendors selling products to Canadian consumers were identified and included. The DISCERN instrument, which consists of 16 questions divided into three sections, was used to evaluate the quality of cannabis-related health information on these websites. Results: Across the 33 websites, the average of the summed DISCERN scores was 36.83 (SD = 9.73) out of 75, and the mean score for overall quality of the publication (DISCERN question 16) was 2.41 (SD=0.71) out of 5. Many of these websites failed to discuss uncertainties in research evidence on cannabis, the impact of cannabis use on quality of life, alternatives to cannabis use, risks associated with cannabis use, and lacked references to support claims on effects and benefits of use.Conclusion: Our findings indicate that the quality of cannabis-related health information provided by online vendors is poor. HCPs should be aware that patients may use these websites as primary sources of information, and appropriately caution patients while directing them to high-quality sources. Future research should serve to replicate this study in other jurisdictions and assess the accuracy of information provided by online cannabis vendors, as this was outside the scope of the DISCERN instrument.
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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 | 0.015 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".