Preliminary results from a community-based ayahuasca-assisted mental health program among a Yaqui Indigenous population in Sonora, Mexico
Bibliographic record
Abstract
Abstract Background The Yaqui Intercultural Medicine Clinic was established in 2020 to provide accessible, culturally-attuned treatment for substance use and mental health disorders among an Indigenous Yaqui community in Mexico. The treatment program integrates ayahuasca ceremonies and psychotherapeutic support within a community-based outpatient treatment service. This observational pilot study was conducted to evaluate the safety, short-term symptom improvement, and cultural implications of the clinic's program. Methods Data from 37 patients who were diagnosed with depression, anxiety, complicated grief, or substance use disorder were included in the analysis. Patients were included in the study if they participated in at least one ayahuasca session with psychotherapeutic support. Data were collected using the Mini International Neuropsychiatric Interview (MINI), Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), Index for Complicated Grief (ICG), and the Post-Traumatic Stress Checklist for DSM-5 (PCL-5). Results Patients demonstrated marked reductions in scores of depression, anxiety, complicated grief, suicidality, and PTSD symptoms following ayahuasca ceremonies. After two ceremonies, mean BDI-II scores dropped from 15.7 to 5.1, mean BAI scores dropped from 16.6 to 6.3, and ICG scores reduced from 39.6 to 10.7. Among eight patients presenting suicide risk, seven cases resolved following a single ayahuasca ceremony. Safety assessment indicated that ayahuasca ceremonies were well-tolerated. Conclusions Preliminary data suggest that ayahuasca ceremonies integrated within a culturally-attuned community-based mental health program show promise for rapidly reducing mental health symptoms among a small group of Yaqui patients. These findings support further research of community-based ayahuasca-assisted therapeutic programs for mental health treatment within Indigenous communities.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".