The Need for Psychedelic-Assisted Therapy in the Black Community and the Burdens of Its Provision
Bibliographic record
Abstract
Psychedelic medicine is an emerging field that examines entheogens, psychoactive substances that produce non-ordinary states of consciousness (NOSC). 3,4-methylenedioxymethamphetamine (MDMA) is currently in phase-3 FDA clinical trials in the United States (US) and Canada to treat the symptoms of posttraumatic stress disorder (PTSD). MDMA is used in conjunction with manualized therapy, because of its effectiveness in reducing fear-driven stimuli that contribute to trauma and anxiety symptoms. In 2017, the FDA designated MDMA as a "breakthrough therapy," signaling that it has advantages in safety, efficacy, and compliance over available medication for the treatment of trauma-, stress-, and anxiety-related disorders such as PTSD. In the US and Canada, historical and contemporary racial mistreatment is frequently experienced by Black people via a variety of macro and micro insults. Such experiences trigger physiological responses of anxiety and fear, which are associated with chronically elevated stress hormone levels (e.g., cortisol and epinephrine), similar to levels documented among those diagnosed with an anxiety disorder. This paper will explore the benefits of entheogens within psychedelic assisted-therapy and their potential benefits in addressing the sequelae of pervasive and frequent negative race-based experiences and promoting healing and thriving among Black, Indigenous and other People of Color (BIPOC). The author(s) discuss the ethical responsibility for providing psychedelic-assisted therapy within a culturally competent provider framework and the importance of psychedelic researchers to recruit and retain BIPOC populations in research and clinical training.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".