Past-Year Lysergic Acid Diethylamide Use in United States Sexual Minorities from 2015 to 2019
Bibliographic record
Abstract
Objective: This study aimed to characterize changes in estimated past-year lysergic acid diethylamide (LSD) use among sexual minorities in the United States using nationally representative data and to compare correlates of LSD use between lesbian, gay, and bisexual individuals (LGB) and heterosexual individuals. Method: Using National Survey on Drug Use and Health data on adults from 2015 to 2019, we investigated correlates of past-year LSD use among LGB individuals compared with heterosexual individuals and changes in the prevalence of past-year LSD use during that period. Correlates were identified with weighted logistic regression. Results: LGB individuals constituted approximately 5% of the sample. From 2015 to 2019 estimated prevalence of past-year LSD use rose 43% in heterosexual individuals, 58% in bisexual individuals, and 106% in lesbian/gay individuals. The relative risk for LSD use ranged from 3.3 to 4.4 times higher for LGB individuals compared with heterosexual individuals during the study period, depending on the year. Interaction analysis found that living in poverty, being divorced versus married, living in a small metropolitan area versus a non-metropolitan area, and past-year suicide attempt were unique correlates of past-year LSD use for heterosexual individuals compared with LGB individuals. Conclusions: Sexual minorities are at increased risk of past-year LSD use compared with heterosexual individuals. There were notable increases in past-year LSD use from 2015 to 2019 among heterosexual, bisexual, and lesbian/gay individuals, with a marked increase in the latter group. These findings highlight the possible need for targeted LSD harm reduction strategies based on sexual identity and more inclusive research on psychedelic use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".