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Record W4412810429 · doi:10.1017/jme.2025.10109

Psychedelic Treatment with Psilocybin: Addressing Medical Malpractice Risk and Physicians’ Concerns

2025· article· en· W4412810429 on OpenAlexafffundabout
Katherine Cheung, Sue-Ling Chang, P. Deschamps, Jean‐Sébastien Fallu, Houman Farzin, Johanne Hébert, Jean-François Stephan, Michel Dorval, Yann Joly

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité du Québec à RimouskiJewish General HospitalInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalUniversité LavalCentre intégré de santé et de services sociaux de Chaudière-AppalachesMcGill University
FundersNational Institutes of HealthCentre Hospitalier Universitaire de QuébecMcGill UniversityUniversité Laval
KeywordsPsilocybinMedical malpracticeLiabilityPsychiatryPsychologyMalpracticeMedicineHallucinogenPolitical scienceLaw

Abstract

fetched live from OpenAlex

Psychedelic treatment with psilocybin is receiving increased attention following clinical trials showing it may help treat end-of-life anxiety, depression, and several other conditions. Despite this, physicians may be reluctant to prescribe psilocybin and carry out psilocybin treatment because of the stigma surrounding psychedelics and the potential for medical malpractice liability. This paper explores whether psilocybin treatment gives rise to a risk of medical malpractice liability for physicians. Following an overview of psilocybin treatment and its regulatory regime in Canada, exploratory vignettes are used to highlight the relevance and limits of malpractice claims. This paper argues that the lack of established medical standards, standardized training, and credentialing contribute to liability risks surrounding psilocybin treatment. More clinical trials, meta-studies of research analyses, and knowledge sharing will help to develop training programs and medical standards of practice to better realize psilocybin's potential.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.203
GPT teacher head0.494
Teacher spread0.291 · 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 designNot applicable
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

Citations1
Published2025
Admission routes3
Has abstractyes

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