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Record W4401210839 · doi:10.47626/2237-6089-2024-0866

The influence of stakeholder interests on safety outcome reporting in psychedelic research and implications for science communication

2024· article· en· W4401210839 on OpenAlexaff
Elena Koning, Marco Solmi, Elisa Brietzke

Bibliographic record

VenueTrends in Psychiatry and Psychotherapy · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of OttawaRoyal Ottawa Mental Health CentreOttawa HospitalQueen's University
Fundersnot available
KeywordsPsychologyStakeholderConsciousnessOutcome (game theory)Mental healthClinical trialPublic relationsPsychotherapistApplied psychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Psychedelics are a group of psychoactive substances that produce complex and subjective changes to consciousness and carry unique safety considerations. There is a growing body of work investigating the use of psychedelics in mental health treatment alongside increasing socio-cultural and political acceptance. This rapid evolution has prompted corporations to fund psychedelic clinical trials, leading to a potential rise in conflicts of interest in relevant studies and publications. However, the body of evidence for the safety and efficacy of psychedelic-assisted psychotherapy is early. There is concern regarding the introduction of bias in psychedelic clinical trials and the selective reporting of results amidst and beyond corporate involvement. At a crucial time in psychedelic drug reform, this paper explores the safety concerns associated with psychedelics, the potential influences of financial stakeholders on safety outcome reporting and the importance of balanced science communication in maintaining public health and safety.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.795
metaresearch head score (Gemma)0.911
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.205
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7950.911
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.008
Science and technology studies0.0090.027
Scholarly communication0.0260.024
Open science0.0060.019
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0060.001

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 head0.275
GPT teacher head0.533
Teacher spread0.259 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReporting
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

Citations4
Published2024
Admission routes1
Has abstractyes

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