Expert Opinion on Psychedelic-Assisted Psychotherapy for People with Psychotic Symptoms
Bibliographic record
Abstract
<title>Abstract</title> Background Currently, personal or familial histories of psychotic symptoms are exclusionary criteria for most psychedelic clinical trials, studies, and treatment programs. This study sought to determine why such an exclusion exists, what the implications of the exclusion criteria are, and if there was agreement in expert opinion. Methods In-depth interviews with 12 experts in the fields of psychiatry, clinical psychology, medicine, and the effects of psychedelics and entheogens were conducted in an expert consultation format. Interviews were transcribed and themes were produced using an Interpretative Phenomenological Analysis (IPA) approach. Results We found that while the exclusion criteria may be justified for psychedelic protocols that provide insufficient psychological support for participants, there was agreement that psychedelic-assisted psychotherapy is not necessarily contraindicated for all individuals with psychotic symptoms. Results suggest that highly supportive psychedelic-assisted psychotherapy may be of benefit to individuals experiencing symptoms of psychosis. Potentially relevant factors for predicting treatment outcomes include specific symptom endorsement, illness duration, symptom severity, quality of the therapeutic alliance, role of trauma in symptom etiology and perpetuation, and the level of other supports in the life of the client.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".