Experiences of psychedelic drug use among people with psychotic symptoms and disorders: Personal growth and mystical experiences
Bibliographic record
Abstract
Abstract Individuals with a history of psychotic experiences and disorders such as schizophrenia, and mood disorders with psychotic features tend to be excluded from psychedelic-assisted therapy research and treatment programs, despite minimal research demonstrating heightened risk of adverse effects for this group. Participants (n = 100) were asked to complete an online, retrospective survey that asked about psychotic experiences and/or diagnoses and one memorable psychedelic experience, along with mental health histories, dose used, set and setting, and other relevant variables including whether they mixed their psychedelic with other substances. Respondents also completed pertinent psychometric questionnaires and answered questions regarding the impact of their psychedelic experience on their well-being, mental health, relationships, spiritual beliefs, and aspects of their life. Thematic inductive analysis was used to identify recurring themes. Most respondents (n = 88) stated that their psychedelic experience resulted in some degree of personal growth. Many also described mystical-type experiences, increased levels of contemplation and spirituality, improved insight, symptomatic improvements, and feelings of love and appreciation following the experience. Most described overall positive experiences, however, 11% (n = 11) described overall negative experiences, which included symptom exacerbation, dysphoria, and terror, and a slightly larger portion described mixed-type experiences.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".