Top Ten Tips Palliative Care Clinicians Should Know About Psychedelic-Assisted Therapy in the Context of Serious Illness
Bibliographic record
Abstract
Psychedelic-assisted therapy (PAT) is a burgeoning treatment with growing interest across a variety of settings and disciplines. Empirical evidence supports PAT as a novel therapeutic approach that provides safe and effective treatment for people suffering from a variety of diagnoses, including treatment-resistant depression, substance use disorder, and post-traumatic stress disorder. Within the palliative care (PC) field, one-time PAT dosing may lead to sustained reductions in anxiety, depression, and demoralization-symptoms that diminish the quality of life in both seriously ill patients and those at end of life. Despite a well-noted psychedelic renaissance in scholarship and a renewed public interest in the utilization of these medicines, serious illness-specific content to guide PAT applications in hospice and PC clinical settings has been limited. This article offers 10 evidence-informed tips for PC clinicians synthesized through consultation with interdisciplinary and international leading experts in the field with aims to: (1) familiarize PC clinicians and teams with PAT; (2) identify the unique challenges pertaining to this intervention given the current legalities and logistical barriers; (3) discuss therapeutic competencies and considerations for current and future PAT use in PC; and (4) highlight critical approaches to optimize the safety and potential benefits of PAT among patients with serious illness and their caregivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".