The Need for Psychedelic-Assisted Therapy in the Black Community and the Burdens of Its Provision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it