The relationships of classic psychedelic use with criminal behavior in the United States adult population
Bibliographic record
Abstract
Criminal behavior exacts a large toll on society and is resistant to intervention. Some evidence suggests classic psychedelics may inhibit criminal behavior, but the extent of these effects has not been comprehensively explored. In this study, we tested the relationships of classic psychedelic use and psilocybin use per se with criminal behavior among over 480,000 United States adult respondents pooled from the last 13 available years of the National Survey on Drug Use and Health (2002 through 2014) while controlling for numerous covariates. Lifetime classic psychedelic use was associated with a reduced odds of past year larceny/theft (aOR = 0.73 (0.65-0.83)), past year assault (aOR = 0.88 (0.80-0.97)), past year arrest for a property crime (aOR = 0.78 (0.65-0.95)), and past year arrest for a violent crime (aOR = 0.82 (0.70-0.97)). In contrast, lifetime illicit use of other drugs was, by and large, associated with an increased odds of these outcomes. Lifetime classic psychedelic use, like lifetime illicit use of almost all other substances, was associated with an increased odds of past year drug distribution. Results were consistent with a protective effect of psilocybin for antisocial criminal behavior. These findings contribute to a compelling rationale for the initiation of clinical research with classic psychedelics, including psilocybin, in forensic settings.
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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 | 0.001 |
| 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".