Selling cannabidiol products in Canada: A framing analysis of advertising claims by online retailers
Bibliographic record
Abstract
BACKGROUND: In Canada, the legalization of cannabis has enabled cannabidiol (CBD) to become a popular commercial product, increasingly used for medical or therapeutic purposes. There are currently over one thousand CBD products available globally, ranging from oil extracts to CBD-infused beverages. Despite increased usage and availability, the evidence supporting the medical efficacy of CBD is limited. Anecdotal evidence suggests CBD sellers represent their products for medical use through direct medical claims or advice, which in Canada, is not allowed under the Cannabis Act without Health Canada approval. However, it is not clear the extent of sellers making health claims or other strategies used to promote medical usage of CBD. The objective of this study is to determine how CBD sellers advertise their products online to consumers. METHODS: 2020 using an automated website scraper tool. A framing analysis was used to determine how CBD sellers frame their products to prospective customers. The specific medical conditions CBD is represented to treat and product forms were tabulated. RESULTS: CBD products are framed to prospective customer through three distinct frames: a specific cure or treatment (n = 1153), a natural health product (n = 872), and a product used in certain ways to achieve particular results (n = 1388). Product descriptions contained medical or therapeutic claims for 171 medical conditions and ailments, with 53.3% of products containing at least one claim. The most prevalent claims found in product descriptions were the ability to treat or manage pain (n = 824), anxiety (n = 609), and inflammation (n = 545). Claims were found for treating or managing serious and-life-threatening illnesses such as multiple sclerosis (n = 210), arthritis (n = 179), cancer (n = 169), Crohn's disease (n = 78), Parkinson's disease (n = 59), and human immunodeficiency virus (HIV) (n = 54). CBD most often came in oil/tincture/concentrate form (n = 755), followed by edibles (n = 428), and vaporizer pen/cartridge/liquid products (n = 290). CONCLUSION: The findings suggest CBD is represented as a medical option for numerous conditions and ailments. We recommend Health Canada to conduct a systematic audit of companies selling CBD for regulatory adherence.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".