Exploring Cultural Competence, Inclusivity, and Diversity in Ketamine Assisted Psychotherapy: A Phenomenological Study
Bibliographic record
Abstract
Black, Indigenous, and People of Color (BIPOC), and other minoritized populations are insufficiently represented in research on therapeutic psychedelics. This research was a phenomenological qualitative exploration of a culturally diverse (Hispanic, African American, Asian, Native American, biracial, or LGBTQIA+) and low-income sample of 15 individuals receiving ketamine-assisted psychotherapy (KAP) at a sliding-scale fee community clinic. Participants were interviewed after a ketamine session, after a ketamine integration session, and one month later. The interviews inquired about mental and emotional state prior to treatment and the treatment context (traditionally called set and setting), preparation for treatment, experiences during the ketamine and integration sessions, barriers to treatment, perceived stigma if any, reflections on KAPs’ impact, and relevance of culture to the treatment. The current analysis, which focuses on participant comments related to diversity, equity, and inclusion that are uniquely relevant to this sample and the research goals, yielded four major themes: Insufficient Financial Resources, Race, Ethnicity, and LGBTQIA+, Stigma, and Culture and Ritual. Themes and subthemes are presented accompanied by representative quotes. Results demonstrate the high salience of culture in the KAP experience and the need to incorporate issues of race, culture, stigma, ritual, and socioeconomic status into treatment planning and outcome research.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".