Exploring THC labelling preferences to communicate the strength of cannabis products: Insights from U.S. consumers
Bibliographic record
Abstract
BACKGROUND: As cannabis policies have become more liberalized internationally, cannabis products have become increasingly accessible, diversified and potent as indicated by the amount of delta-9-tetrahydrocannabinol (THC) they contain. The THC content of cannabis products is often inconsistently reported, limiting opportunities to inform consumers about health risks and safer consumption practices. We explored consumers' preferences on the type of THC information (i.e., standard units, concentration, total content) that should be displayed on cannabis products in legal markets. METHODS: A convenience sample of 575 adults from various U.S. states who reported cannabis use within the past 12 months was recruited via Amazon Mechanical Turk. Respondents completed a survey assessing cannabis use and related attitudes, which included a subsection focused on potential metrics that could be used to report THC content. Descriptive and inferential statistical analyses were conducted. RESULTS: Majority of respondents considered it important for cannabis products to include information on Standard THC Units (e.g., 5 milligrams of THC), THC concentration (%), or the total content of THC on cannabis product labels. When comparing Standard THC Units, THC concentration or both options, Standard THC Units were the preferred metric, p<.001. Consumer preferences for these three metrics did not signficantly differ across U.S. state cannabis policy environments, sex, and frequency of cannabis use when compared using multinomial logistic regression. CONCLUSIONS: These exploratory findings preliminarily support the potential value of standardized THC dose labelling, particularly in the form of a standardized metric such as the Standard THC Unit, as a tool to better inform consumer decision-making and promote safer patterns of use. The findings require replication in more representative samples using additional THC metrics, including but not limited to, THC milligrams as a response option.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".