Psychedelic-Assisted Therapy Training: An Argument in Support of Firsthand Experience of Nonordinary States of Consciousness in the Development of Competence
Bibliographic record
Abstract
Introduction: This perspective on experiential training delves into the potential benefits and counterarguments related to integrating firsthand experience of psychedelic-assisted therapy (PaT) to enhance the competency of trainees. The Case for Experiential Training as a Core PaT Competency: Experiential training serves a dual purpose: promoting therapists' mental wellness and refining their skills in facilitating healing in nonordinary states of consciousness. With a rising demand for PaT amid a growing mental health crisis, therapists are increasingly seeking PaT training, including experiential training from underground sources. Educators actively strive to establish formal PaT competencies and training standards, recognizing the need to consider both perspectives in this discourse. Counter Arguments: The emergence of differing opinions on the therapeutic value of firsthand exposure to PaT and concerns about potential bias underscores the necessity for further research to substantiate claims on both sides. Access: Whether or not consensus is achieved, the persistent demand for experiential training remains. Offering this form of training in regulated settings has the potential to reduce reliance on illicit sources for this sought-after form of training, ensuring a more controlled and ethical approach.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".