THC labeling on cannabis products: an experimental study of approaches for labeling THC servings on cannabis edibles
Bibliographic record
Abstract
BACKGROUND: Over-consumption is a common adverse outcome from cannabis edibles. States such as Colorado require each serving of cannabis edible to carry a THC symbol. This study aimed to test whether packaging edibles in separate servings and/or indicating the THC level per serving improves consumer understanding of serving size. METHODS: An 3 × 2 experimental task was conducted as part of the 2019 International Cannabis Policy Study online survey. Respondents from Canada and the US (n = 45,504) were randomly assigned to view an image of a chocolate cannabis edible. Packages displayed THC labels according to 1 of 6 experimental conditions: packaging (3 levels: whole multi-serving bar; individual chocolate squares; separately packaged squares) and THC stamp (2 levels: stamp on each square vs. no stamp). Logistic regression tested the effect of packaging and THC stamp on odds of correctly identifying a standard serving, among edible consumers and non-consumers separately. Edible consumers were also asked about their awareness of a standard THC serving. RESULTS: Only 14.6% of edible consumers reported knowing the standard serving of THC for cannabis edibles. In the experimental task, among non-consumers who saw stamped bars, the multi-serving bar (AOR = 1.16 (1.08, 1.24) p < 0.001) and individually packaged squares (AOR = 1.08 (1.01, 1.16), p = 0.031) elicited more correct responses than individual squares. There was no difference in packaging formats when stamps were absent (p > 0.05 for all). Among edible consumers, there was no effect of the packaging (p = 0.992) or stamp manipulation (p = 0.988). Among both edible consumers and non-consumers, respondents in US states with legal recreational cannabis performed better than Canadians (p < 0.001). CONCLUSIONS: Regulations that require THC information to be stamped or indicated on each serving of cannabis edible may facilitate understanding of how much to consume, especially among novice consumers.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".