Product Characteristics, Warnings, and Marketing Appeals Conveyed on Delta-8 THC Product Packaging in the United States and Canada
Bibliographic record
Abstract
Objective Delta-8 tetrahydrocannabinol (delta-8 THC) cannabis products proliferated in the United States (US) following the 2018 Farm Bill and are marketed in Canada. This study assessed characteristics and marketing appeals shown on exterior product packaging for a sample of US and Canadian delta-8 THC products. Method Delta-8 THC packaging photos were obtained from US and Canadian respondents from the 2021 and 2022 International Cannabis Policy Study. A content analysis assessed cannabinoid content labels, presence and placement of health warnings, and marketing appeals (e.g., “hemp” descriptors, cartoons). Packages were double-coded. Results The sample (N=140 products) included ingestibles (43.6%, n=61); vapes (37.9%, n=53); dried flower (7.9%, n=11); oral liquids (6.4%, n=9); and pre-rolls, topicals, and concentrates (1.4%, n=2 each). Fifteen percent (n=21) listed cannabinoids in addition to delta-8 THC; 6.4% (n=9) listed other intoxicating cannabinoids (delta-9 THC, delta-10 THC, and/or HHC). Intoxicating cannabinoid content (including delta-8 THC) per piece and per pack was specified for 82% (n=50/61) and 73.8% (n=45/61) of ingestibles, respectively. A minority of vapes (17%, n=9/53), dried flower (27.3%, n=3/11), and oral liquids (33.3%, n=3/9) stated concentration. Warnings were observed on 32.9% of products (n=46), including 11.7% (n=16/137) of primary surfaces and 67.4% (n=31/46) of secondary surfaces. Marketing appeals included “hemp” descriptors (43.6%, n=61), cannabis symbols (28.6%, n=40), referencing delta-8 THC’s legality (25.7%, n=36), and cartoons (21.4%, n=30). Conclusions Delta-8 THC products come in many forms and often lack health warnings and details about cannabinoid content. Studies assessing how delta-8 THC product packaging information impacts use patterns are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".