Supporting Meaningful Choices: A Decision Aid for Individuals Facing Existential Distress and Considering Psilocybin-Assisted Therapy
Bibliographic record
Abstract
Background/Objectives: Given the limitations of traditional approaches to treating existential distress in seriously ill patients, psilocybin-assisted therapy (PAT) has emerged as a promising treatment option. However, weighing up the potential risks and benefits of this approach can be challenging for both healthcare professionals and patients. Decision aids can play a key role in supporting shared decision making by clarifying options, improving knowledge, and enhancing decision quality. To date, there is no decision aid specific to PAT. This descriptive study aimed to develop a decision aid for individuals considering this therapy. Methods: A paper-based/electronic decision aid was developed with a multidisciplinary steering committee following the International Patient Decision Aids Standards Collaboration (IPDAS). Development included conducting a literature review and prototype design, evaluating acceptability and usability by potential users (i.e., patients and healthcare professionals), and producing a final version. Questionnaires, direct feedback, and semi-structured interviews with potential users allowed for evaluation and refinement of design and content. Results: The final version of the decision aid is presented as a booklet, covering areas such as PAT education, comparison of treatment options, and personal reflection. Feedback from patients (n = 5) and healthcare professionals (n = 5) guided improvements, helping clarify content, ensuring balanced information, optimizing its length for usability, and providing decision-making support. Conclusions: The decision aid developed in this study demonstrated satisfactory acceptability and usability, meeting IPDAS criteria. By providing balanced and accessible information, it may facilitate shared decision-making for individuals considering PAT, representing a significant step forward in this emerging area of palliative 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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".