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Record W4389614011 · doi:10.3138/jmvfh-2023-0014

Mechanisms of integration in psychedelic-assisted therapy

2023· article· en· W4389614011 on OpenAlexaffvenue
Nicole S. Coverdale, Douglas J. Cook

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsPsychologyPsychotherapistClinical psychologyNeuroticismOpenness to experienceRandomized controlled trialCognitionPersonalityMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

LAY SUMMARY Psychedelic pharmacotherapies combined with structured psychotherapy have shown promise in the treatment of several psychological conditions. This type of therapy is known as psychedelic-assisted psychotherapy (PAP) and includes three phases: preparation, in-session support, and integration. The purpose of this review was to identify randomized controlled trials (RCTs) that used psychedelics to treat a psychological condition and to summarize the literature on changes that may be associated with clinical outcomes, as measured with MRI and various psychologically based tools. Psychedelics were administered in 17 RCTs, and 16 of these did so within a PAP framework. A total of 19 studies were identified that looked at MRI or psychological outcomes during the integration phase. Changes in brain networks during integration were identified but were not consistent between studies because of small sample sizes and inconsistent methodology. Some evidence suggests that changes in the executive control network may occur after psychedelic administration. Psychological changes after psychedelic administration were related to cognitive flexibility and personality traits such as openness and neuroticism. Overall, studies in this field should be repeated with a greater number of participants and other MRI-based techniques.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.107
GPT teacher head0.396
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

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