Improving Access to Psilocybin-Assisted Therapy: Barriers, Challenges, and Recommendations
Bibliographic record
Abstract
Introduction Psilocybin-assisted therapy (PAT) has demonstrated significant potential in alleviating anxiety, depression, and psychological distress among individuals with terminal illnesses. However, numerous barriers prevent equitable access to this transformative treatment. Objectives This study seeks to gather the perspectives of patients on the waitlist of PAT. Methods Semi-structured interviews highlight the challenges faced by patients seeking PAT and their care providers and propose recommendations to enhance accessibility. Results Through a case study of Roots to Thrive, a non-profit healthcare practice offering group-based PAT, obstacles such as complex application processes, fear of judgement, logistical and financial constraints, and systemic inequities are revealed. Moreover, Health Canada’s stringent control of PAT access via clinical trials and the Special Access Program (SAP) presents challenges for primary care providers and hinders the involvement of trained practitioners. The moral distress experienced by patients and providers due to delayed or denied access further emphasizes the urgency of addressing these barriers. Conclusions Advocates are calling for streamlined referral systems, expedited services for end-of-life patients, formal billing infrastructure, practitioner education, expanded coverage, legislative adjustments, post-therapy support, and collaboration with non-profit organizations and Indigenous Healers to promote equitable and effective PAT. By implementing these recommendations, barriers to PAT can be overcome, allowing more individuals to benefit from this therapy and find relief from the psychological distress associated with their conditions. 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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".