Developing an Ethics and Policy Framework for Psychedelic Clinical Care: A Consensus Statement
Bibliographic record
Abstract
Importance: As government agencies around the globe contemplate approval of the first psychedelic medicines, many questions remain about their ethical integration into mainstream medical practice. Objective: To identify key ethics and policy issues related to the eventual integration of psychedelic therapies into clinical practice. Evidence Review: From June 9 to 12, 2023, 27 individuals representing the perspectives of clinicians, researchers, Indigenous groups, industry, philanthropy, veterans, retreat facilitators, training programs, and bioethicists convened at the Banbury Center at Cold Spring Harbor Laboratory. Prior to the meeting, attendees submitted key ethics and policy issues for psychedelic medicine. Responses were categorized into 6 broad topics: research ethics issues; managing expectations and informed consent; therapeutic ethics; training, education, and licensure of practitioners; equity and access; and appropriate role of gatekeeping. Attendees with relevant expertise presented on each topic, followed by group discussion. Meeting organizers (A.L.M., I.G.C., D.S.) drafted a summary of the discussion and recommendations, noting points of consensus and disagreement, which were discussed and revised as a group. Findings: This consensus statement reports 20 points of consensus across 5 ethical issues (reparations and reciprocity, equity, and respect; informed consent; professional boundaries and physical touch; personal experience; and gatekeeping), with corresponding relevant actors who will be responsible for implementation. Areas for further research and deliberation are also identified. Conclusions and Relevance: This consensus statement focuses on the future of government-approved medical use of psychedelic medicines in the US and abroad. This is an incredibly exciting and hopeful moment, but it is critical that policymakers take seriously the challenges ahead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".