Ethnoracial health disparities and the ethnopsychopharmacology of psychedelic-assisted psychotherapies.
Bibliographic record
Abstract
Emerging evidence from randomized, double-blind, placebo-controlled clinical trials suggests psychedelic compounds such as 3,4-methylenedioxymethamphetamine (MDMA), psilocybin, and lysergic acid diethylamide (LSD), when administered as an adjunct to psychotherapy, that is, psychedelic-assisted psychotherapy (PAP), may be beneficial for treating substance use disorders, posttraumatic stress disorder (PTSD), depression, anxiety, and other psychiatric conditions. Previous ethnopsychopharmacological research has identified ethnoracial differences in the metabolism, safety, and efficacy of psychotropic drugs, yet no studies have directly investigated the impact of ethnoracially based differences in psychedelic drug pharmacology. Although there is an extensive global history of psychedelic use among peoples of various cultures, ethnicities, and intersectional identities, psychedelic research has been conducted almost exclusively on White populations in North America and Western Europe. The failure to include Black, Indigenous, and People of Color (BIPOC) in psychedelic research trials neglects the ethnic, racial, and cultural factors that may impact individual responses to PAP and thereby prevents generalizability of findings. This article investigates the impact of biological and social factors related to culture, ethnicity, and race on pharmacological responses to PAP, as well as clinical outcomes. The limitations of ethnopsychopharmacology are discussed, and the authors present expected cultural, clinical, and public health benefits of expanding funding for this area. This work will draw attention to the unique and individualized needs of ethnoracially diverse clients in therapeutic settings and is intended to inform future PAP trials. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".