Pilot Evaluation of a Residential Drug Addiction Treatment Combining Traditional Amazonian Medicine, Ayahuasca and Psychotherapy on Depression and Anxiety
Bibliographic record
Abstract
Recent research highlighted the therapeutic potential of ayahuasca, a psychoactive plant brew used ritually in traditional Amazonian medicine (TAM). The present study evaluates the impact of integrating ayahuasca and TAM with psychotherapy on depression and anxiety in an inpatient addiction treatment program. Male patients (N = 31) were evaluated pre and post treatment using the Beck Anxiety Inventory (BAI) and the Beck Depression Inventory (BDI). Clinical and sociodemographic characteristics, motivation, quality of life, spirituality, and treatment satisfaction were also measured and analyzed by means of two tailed t-test, one way ANOVA and Spearman test. From pre- to post-treatment, patients showed significant reductions in scores of anxiety (from 20.8 to 11.6, p < .002) and depression (from 18.7 to 7.5, p <.001). Similarly, patients showed higher scores of quality of life (p < .001) and spirituality (p < .001) upon discharge, which correlated with their reduction in scores of anxiety and depression. While future results will evaluate the efficacy of this treatment on measures of addiction at follow-up, the present results build upon previous research to bring further support to the use of Ayahuasca and Amazonian medicine in mental health treatments with a transpersonal focus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".