A Cohort Based Case Series: Learnings from an Iterative Group Therapy Model to Support Psilocybin-Assisted Therapy for Patients with a Terminal Diagnosis
Bibliographic record
Abstract
Introduction While much is known about psilocybin-assisted therapy for individuals, little is known about the experience of participants in a group psilocybin therapy model. Objectives In an attempt to bridge this gap in the literature, a program development and quality improvement effort was launched. Methods Thirty-one psychedelic-assisted therapy (PaT) sessions were provided for 25 participants within four iterative cohorts over the span of one year. This article reports participant feedback in an effort to inform the benefits and challenges of group-administered PaT. Results Six to eight once-weekly group resilience-based community of practice (CoP) sessions were combined with one psilocybin-assisted therapy session for patients experiencing distress related to a terminal health condition. The virtual hybrid group therapy model is research informed, with a curriculum that provides knowledge-based content, combined with the relational elements necessary to successfully deliver group-administered psilocybin-assisted therapy. Twenty one of the twenty five participants (84%) completed the program. Based on participant feedback, the following themes emerged: 1) Improvement of pre-treatment preparation sessions; 2) PaT Benefits: Gaining perspective, peace, non-attachment, authenticity, honesty, relational capacity; 3) The community of practice (CoP) as the primary conduit for connection and regulation 4) Population specific curriculum with a greater focus on how to navigate death, pain and loss; 5) PaT session Challenges; 6) The interpersonal and support capacity of the team as critical for the overall experience. Conclusions While more research is needed, results suggest that psilocybin can be delivered safely in a group setting, and that a virtual CoP is effective across the spectrum of set, setting and integration Our findings also suggest that there is much to learn - and improve upon - in this novel area of service delivery. Disclosure of Interest None Declared
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".