Bibliographic record
Abstract
Despite significant advances in symptom management for patients affected by serious illness, physicians lack effective legal treatments for individuals suffering from demoralization, death anxiety, and existential distress. Psilocybin-assisted psychotherapy employs psilocybin-containing mushrooms or synthetic psilocybin grounded in indigenous traditions and within the context of a therapeutic mindset and environment ("set and setting") to achieve altered states of consciousness that promote healing and psychospiritual growth while reducing suffering. Current research evidence suggests that this form of therapy could serve as a safe and effective therapeutic tool for such patients. This presentation will describe a case series of patients with advanced cancer who received physician-supervised home-based psilocybin-assisted psychotherapy in Montreal, Canada. Our experience postulates the safety and efficacy of this laborious treatment process. By executing this clinical practice in the public healthcare system of Quebec for the first time, we have made an attempt to provide equitable access to these clinical therapies. Having performed these treatments outside the context of clinical trials, we have been able to tailor the therapeutic frame and treatment approach to a more patient-centric and culturally-informed manner. That being said, given the existing societal discrimination and stigma against this form of therapy, including by healthcare professionals, there remain further barriers to overcome in the equitable provision of care, especially to certain segments of the population. The authors will discuss these and potential solutions to addressing them.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".