Beyond prohibition: A public health analysis of naturalistic psychedelic use
Bibliographic record
Abstract
Abstract Psychedelic drug use is experiencing a global resurgence, both in clinical research and community settings. This paper presents a comprehensive public health analysis of the naturalistic use of psychedelics—defined as use outside clinical or research environments. Drawing on a review of 104 peer-reviewed articles, this analysis evaluates the mental, physical, and social outcomes associated with substances such as psilocybin, LSD, MDMA, mescaline, and 5-MeO-DMT. Findings indicate that naturalistic psychedelic use is associated with reductions in depression, anxiety, PTSD, substance use disorders, interpersonal violence, and suicidality, while enhancing emotional well-being, social connectedness, spirituality, nature relatedness, psychological flexibility and physical health. These benefits are observed across diverse populations in many countries, including individuals with trauma, addictions, and chronic pain, as well as in older adults and marginalized groups. Importantly, while adverse effects can occur, they are typically short-lived and often associated with identifiable risk factors such as youth, high doses, psychological vulnerability, and poor set and setting. Drawing on harm reduction principles and Indigenous cultural models, the paper outlines how public education and safe use guidelines—emphasizing mindset, environment, and dosage—can mitigate risks. The data suggest that current prohibitionist drug policies are both outdated and harmful and that a shift toward legalization, regulated access, and evidence-informed education is not only justified but urgently needed. A public health approach to psychedelics, one grounded in safety, inclusion, and scientific evidence, offers the most rational path forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".