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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".