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Record W4396509933 · doi:10.1089/psymed.2023.0004

Psychedelic-Assisted Therapy Training: An Argument in Support of Firsthand Experience of Nonordinary States of Consciousness in the Development of Competence

2024· review· en· W4396509933 on OpenAlexafffund
Shannon Dames, Crosbie Watler, Pamela Kryskow, Pearl Allard, Michelle Gagnon, Wes Taylor, Vivian W. L. Tsang

Bibliographic record

VenuePsychedelic Medicine · 2024
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of British ColumbiaVancouver Island University
FundersVancouver Island University
KeywordsConsciousnessCompetence (human resources)PsychologyArgument (complex analysis)PsychotherapistMedical educationMedicineSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.293
GPT teacher head0.487
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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