Exploring ayahuasca‐assisted therapy for addiction: A qualitative analysis of preliminary findings among an Indigenous community in Canada
Bibliographic record
Abstract
INTRODUCTION AND AIMS: A previous observational study of ayahuasca-assisted therapy demonstrated statistically significant reductions in self-reported problematic cocaine use among members of an Indigenous community in Canada. This paper aims to qualitatively explore the impact of ayahuasca-assisted therapy on addiction and other substance use-related outcomes and elucidate the lived experiences of participants. DESIGN AND METHODS: Qualitative interviews were conducted with 11 adult Indigenous participants of the ayahuasca-assisted 'Working with Addiction and Stress' ceremonial retreats (June-September 2011). Semi-structured interviews assessed experiences of participants following the retreats at 6-month follow up. Thematic analysis of interview transcripts was conducted. RESULTS: Narratives revealed that the retreats helped participants identify negative thought patterns and barriers related to their addiction in ways that differed from conventional therapies. All participants reported reductions in substance use and cravings; eight participants reported complete cessation of at least one substance at follow up. Increased connectedness with self, others and nature/spirit was described as a key element associated with reduced substance use and cravings. DISCUSSION AND CONCLUSIONS: This analysis expands upon prior quantitative results highlighting the therapeutic potential of ayahuasca-assisted therapy and provides important contextual insights into why ayahuasca-assisted therapy may have been beneficial for members of an Indigenous community seeking to address their problematic use of substances. Given limited efficacy of conventional treatments for resolving addiction issues, further research should investigate the role of ayahuasca and other psychedelic-assisted therapies in enhancing connectedness and other key factors that may improve well-being and reduce harmful substance use.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".