Exploring barriers and opportunities for Black, Indigenous, and People of Color (BIPOC) therapists in psychedelic-assisted therapy: A qualitative study
Bibliographic record
Abstract
Abstract Background and aims Psychedelic-assisted therapies (PAT) have emerged as promising treatments for mental health conditions such as depression, anxiety, and trauma-related disorders. However, the underrepresentation of Black, Indigenous, and People of Color (BIPOC) in PAT research and clinical practice limits equitable access and the generalizability of outcomes. This study explored the perspectives of BIPOC therapists on opportunities and barriers to engaging in PAT, with the aim of identifying strategies to foster inclusivity and equity in the field. Methods Using a qualitative design, we conducted semi-structured interviews and focus groups with 18 licensed therapists and healthcare professionals identifying as BIPOC. Data collection explored participants' experiences with PAT, perceived barriers and opportunities, and perspectives on cultural diversity within the field. Template analysis was conducted on transcribed audio recordings. Results Key ‘Opportunities’ themes included the potential for BIPOC therapists to enhance diversity in PAT, foster safety and trust for clients of color, and provide culturally attuned care to address trauma rooted in systemic oppression. Participants identified ‘Barriers’ such as financial and geographic inaccessibility of training, exclusionary training environments, stigma associated with psychedelics, and the extractive nature of current Western therapeutic models. To address these challenges, participants recommended culturally responsive training programs, mentorship opportunities, financial support, and community education to reduce stigma and increase engagement. Most participants identified as African American/Black, which may limit transferability of findings to other BIPOC groups. Conclusion This study underscores the critical role of BIPOC therapists in advancing equity and cultural responsiveness in PAT and highlights the need for intentional efforts to create equitable, culturally informed models of care.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".