Defining the Roles and Research Priorities for Psychedelic-Assisted Therapies in Patients with Serious Illness: Expert Clinicians' and Investigators' Perspectives
Bibliographic record
Abstract
Background: Recent and preprohibition studies show that patients with serious illness might benefit from psychedelic-assisted therapies for a range of symptoms, physical, psychosocial, and existential. Objective: To explore the potential roles and research priorities of these therapies in patients with serious illness. Design, Setting, and Participants: Qualitative study based on semistructured interviews with 17 experts in serious illness care and/or psychedelic research from the United States and Canada. Measurements: The interview guide elicited participants' perspectives on (1) the potential roles of psychedelic-assisted therapies in this setting, (2) research priorities relevant to this population, and (3) the potential for integrating psychedelic-assisted therapies into existing delivery models of serious illness care. We used thematic analysis until thematic saturation. Results: Domain I: Participants had polar views on the therapeutic potential of psychedelic-assisted therapies, ranging from strong beliefs in their medical utility to reluctance about their use in this patient population. They shared concerns related to the risks of adverse effects, such as delirium or worsening of psychological distress. Domain II: Research priorities primarily concerned patients with clinically diagnosed psychosocial distress, such as depression, anxiety, or demoralization. Participants also articulated potential roles extending beyond traditional medical diagnosis. Domain III: Participants emphasized essential safety and efficacy guidelines relevant to the integration of these therapies into existing models of care. Conclusion: This qualitative study highlights issues and priorities for research on psychedelic-assisted therapies in patients with serious illness and proposes a conceptual framework for integrating these therapies into existing delivery models of serious illness care.
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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 | 0.235 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".