On Minimizing Risk and Harm in the Use of Psychedelics
Bibliographic record
Abstract
Objective: This article outlines recommendations from 30 psychedelic researchers on how to create a better psychedelic safety net. Methods: A survey of 30 psychedelic researchers asked them to identify key critical research gaps around psychedelic harm and safety. Results: The critical research gaps identified by the authors included defining the main types of psychedelic harm, the predictors of those harms, and the most effective way to treat those harms. They also call for better support for those experiencing post-psychedelic difficulties, including better online information, peer support groups, affordable therapy, and psychiatric consultation and medication. Finally, the authors call for better funding to create a psychedelic safety net, and suggest psychedelic philanthropists, investors and companies could commit 1% of their investment in psychedelics into supporting safety measures such as research and support services. Conclusions: The authors identify several practical steps to create a better psychedelic safety net and call for more funding to psychedelic safety measures such as research and support services. Relevance to clinical practice: The authors outline important gaps in our knowledge around the safety and risk profile of psychedelic medicines and identify practical steps forward for researchers and clinical practitioners to make this promising field safer.
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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 | 0.106 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".