Self‐reported <scp>THC</scp> content and associations with perceptions of feeling high among cannabis consumers
Bibliographic record
Abstract
INTRODUCTION: Few studies have examined consumer knowledge and communication of tetrahydrocannabinol (THC) content among cannabis consumers, including potential differences by cannabis legislation. The study sought to: (i) examine self-reported knowledge of THC content across 10 cannabis products; (ii) examine self-reported intoxication levels; and (iii) examine association between self-reported THC levels and intoxication levels. METHODS: Repeat cross-sectional surveys were conducted in Canada and the United States in September-November 2020 as part of the International Cannabis Policy Study. Respondents were past 12-month cannabis consumers, aged 16-65 (n = 13,689). A weighted logistic regression model examined the association between expected intoxication of dried flower, jurisdiction and frequency of cannabis use. RESULTS: Across all 10 cannabis products, approximately two-thirds of consumers did not know the quantitative THC level of the cannabis product they last used. Qualitative levels of THC (e.g., 'low' or 'high' THC) showed moderate correspondence with quantitative self-reported THC levels for most products. Approximately half of consumers across all products reported achieving their desired intoxication level at last use, with higher levels among more frequent consumers and Canadian consumers of dried flower (F = 2.54, p = 0.019). DISCUSSION AND CONCLUSIONS: Overall, comprehension of THC levels in cannabis products is low among consumers in both illegal and legal markets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".