Ayahuasca-Assisted Therapy for Addiction: Results from a Preliminary Observational Study in Canada
Bibliographic record
Abstract
INTRODUCTION: This paper reports results from a preliminary observational study of ayahuasca-assisted treatment for problematic substance use and stress delivered in a rural First Nations community in British Columbia, Canada. METHODS: The "Working with Addiction and Stress" retreats combined four days of group counselling with two expert-led ayahuasca ceremonies. This study collected pre-treatment and six months follow-up data from 12 participants on several psychological and behavioral factors related to problematic substance use, and qualitative data assessing the personal experiences of the participants six months after the retreat. FINDINGS: Statistically significant (p < 0.05) improvements were demonstrated for scales assessing hopefulness, empowerment, mindfulness, and quality of life meaning and outlook subscales. Self-reported alcohol, tobacco and cocaine use declined, although cannabis and opiate use did not; reported reductions in problematic cocaine use were statistically significant. All study participants reported positive and lasting changes from participating in the retreats. CONCLUSIONS: This form of ayahuasca-assisted therapy appears to be associated with statistically significant improvements in several factors related to problematic substance use among a rural aboriginal population. These findings suggest participants may have experienced positive psychological and behavioral changes in response to this therapeutic approach, and that more rigorous research of ayahuasca-assisted therapy for problematic substance use is warranted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".