People of color in North America report improvements in racial trauma and mental health symptoms following psychedelic experiences
Bibliographic record
Abstract
This study examined how psychedelics reduced symptoms of racial trauma among black, indigenous, and people of color (BIPOC) subsequent to an experience of racism. A cross-sectional internet-based survey included questions about experiences with racism, mental health symptoms, and acute and enduring psychedelic effects. Changes in mental health were assessed by retrospective report of symptoms in the 30 days before and 30 days after an experience with psilocybin, Lysergic acid diethylamide (LSD), or 3,4-Methylenedioxymethamphetamine (MDMA). We recruited 313 diverse BIPOC in the US and Canada. Results revealed a significant (p < .001) and moderate (d = −.45) reduction in traumatic stress symptoms from before-to-after the psychedelic experience. Similarly, participants reported decreases in depression (p < .001; d = −.52), anxiety (p < .001; d = −.53), and stress (p < .001; d = −.32). There was also a significant relationship (Rc = 0.52, p < .001) between the dimension of acute psychedelic effects (mystical-type, insight, and challenging experiences) and decreases in a cluster of subsequent psychopathology (traumatic stress, depression, anxiety, and stress), while controlling for the frequency of prior discrimination and the time since the psychedelic experience. BIPOC have been underrepresented in psychedelic studies. Psychedelics may decrease the negative impact of racial trauma. Future studies should examine the efficacy of psychedelic-assisted therapy for individuals with a history of race-based trauma.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".