The quality of health information provided on web sites selling cannabis to consumers in Canada is poor
Bibliographic record
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 easy access and convenience to consumers. The objective of this study was to evaluate the quality of health information provided by web sites 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 web sites 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 web sites. RESULTS: Across the 33 web sites, the average of the summed DISCERN scores was 36.83 (SD = 9.73) out of 75, and the mean score for the overall quality of the publication (DISCERN question 16) was 2.41 (SD = 0.71) out of 5. Many of these web sites 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. Healthcare providers should be aware that patients may use these web sites 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".