Medicinal psychedelics for mental health and addiction: Advancing research of an emerging paradigm
Bibliographic record
Abstract
The medical use of psychedelic substances (e.g. psilocybin, ayahuasca, lysergic acid diethylamide and 3,4-methylenedioxymethamphetamine) is attracting renewed interest, driven by a pressing need for research and development of novel therapies for psychiatric disorders, as well as promising results of contemporary studies. In this Viewpoint, we reflect upon the ‘Clinical Memorandum on Psychedelics’ recently released by the Royal Australian and New Zealand College of Psychiatrists and note subsequent developments including the application for down-scheduling of psilocybin and 3,4-methylenedioxymethamphetamine presently being considered by the Therapeutic Goods Administration and approvals for access via the Special Access Scheme. We suggest that this field is worthy of rigorous research to assess potential benefits, address safety parameters and clarify therapeutic mechanisms. To this end, we outline recent research findings, provide an overview of current knowledge relating to mechanisms of action and discuss salient aspects of the psychedelic-assisted psychotherapy treatment model. The sum of this research points towards medicinal psychedelics as a potential new class of psychiatric treatments when used within a medically supervised framework with integrated psychotherapeutic support. However, before widespread translation into clinical use can occur, appropriately designed and sufficiently powered trials are required to detect both potential positive and negative outcomes. Unique safety and regulatory challenges also need to be addressed. As for any new medical therapy, psychedelic research needs to be conducted in a rigorous manner, through the dispassionate lens of scientific enquiry. Carte blanche availability to practitioners, without specific protocols and appropriate training, would be potentially harmful to individuals and detrimental to the field.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".