Prevalence of Blunt and Hemp Wrap Use Among Young Adults in the United States, 2022
Bibliographic record
Abstract
Blunt and hemp wraps, as a means of consuming cannabis, have emerged into the retail space where the prevalence has been increasing since 2017. There is limited epidemiological research on the prevalence of use of these products across the U.S. particularly among young adults who are at greater risk of tobacco and cannabis use. This study draws from a U.S. national representative sample of young adults (n = 1178) captured in May 2022. Respondents participated in an online survey about their use of blunt and hemp wraps. Multinomial regression was used to examine differences in sociodemographic characteristics (gender, race/ethnicity, sexual orientation, educational attainment, and region) in relation to use of each wrap type. One quarter (22.7%) of young adults reported ever having used a blunt wrap, 3.2% in the past 30-days. One in seven (14.3%) had ever used a hemp wrap, 2.3% in the past 30-days. Non-Hispanic Black young adults were 1.55 and 2.91 times as likely to have ever used blunt or hemp wraps, respectively, compared to non-Hispanic Whites. Similarly, participants who identified as gay or lesbian or bisexual similarly had greater odds of having ever used blunt or hemp wraps. Hispanic young adults were 2.49 times as likely to have used hempwraps compared to non-Hispanic Whites. Blunt and hemp wrap use is prevalent among young adults, particularly among minoritized populations. Continued research and surveillance of use of these products is needed to fully evaluate the impact their use may have on the broader population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".