The CAnadian Network for Psychedelic-Assisted Cancer Therapy (CAN-PACT): A Multi-Phase Program Overview
Bibliographic record
Abstract
The CAnadian Network for Psychedelic-Assisted Cancer Therapy (CAN-PACT) was launched in 2025 to address urgent gaps in supportive care for Canadians with cancer experiencing demoralization syndrome (loss of meaning, dysphoria, disheartenment, helplessness, a sense of failure) and related psychosocial distress. CAN-PACT has six major objectives: (1) to develop a national interdisciplinary research and practice network; (2) to set research priorities through structured stakeholder engagement; (3) to develop and provide PAT training and education for clinicians, researchers, and patients; (4) to pilot test the feasibility of intervention and assessment procedures; (5) to conduct a multi-center, randomized controlled trial of PAT for people with advanced cancer; and (6) to inform and influence healthcare policy on PAT in Canada. We discuss the background and need for PAT in cancer, describe challenges currently limiting its use, and outline CAN-PACT's strategy for building capacity, generating Canadian evidence, and preparing the oncology healthcare environment for potential implementation. This manuscript presents a summary overview of CAN-PACT as a multi-objective research program; detailed protocols for each discrete study component will be published separately as the research program progresses. Through environmental scans, national engagement, targeted training, rigorous research, and ongoing collaboration with policymakers, CAN-PACT aims to enable equitable access to safe, evidence-based PAT for people with advanced cancer in Canada's publicly funded cancer centers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".