An international Delphi consensus for reporting of setting in psychedelic clinical trials
Bibliographic record
Abstract
Psychedelic substances exhibit complex interactions with the 'set and setting' of use, that is, the mental state of the user and the environment in which a psychedelic experience takes place. Despite these contextual variables' known importance, psychedelic research has lacked methodological rigor in reporting extra-pharmacological factors. This study aimed to generate consensus-based guidelines for reporting settings in psychedelic clinical research, according to an international group of psychedelic researchers, clinicians and past trial participants. We conducted a Delphi consensus study composed of four iterative rounds of quasi-anonymous online surveys. A total of 89 experts from 17 countries independently listed potentially important psychedelic setting variables. There were 770 responses, synthesized into 49 distinct items that were subsequently rated, debated and refined. The process yielded 30 extra-pharmacological variables reaching predefined consensus ratings:i.e., 'important' or 'very important' for ≥70% of experts. These items compose the Reporting of Setting in Psychedelic Clinical Trials (ReSPCT) guidelines, categorized into physical environment, dosing session procedure, therapeutic framework and protocol, and subjective experiences. Emergent findings reveal significant ambiguities in current conceptualizations of set and setting. The ReSPCT guidelines and accompanying explanatory document provide a new standard for the design and documentation of extra-pharmacological variables in psychedelic clinical research.
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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 | 0.661 | 0.570 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".