A century of research on psychedelics: A scientometric analysis on trends and knowledge maps of hallucinogens, entactogens, entheogens and dissociative drugs
Bibliographic record
Abstract
A scientometric analysis was realized to outline clinical research on psychedelics over the last century. Web of Science Core Collection was searched up to March 18, 2022, for publications on psychedelics. Network analyses and bibliometrics were combined, to identify research themes and trends with Bibliometrix and CiteSpace. The primary aim was to measure research trends evolution over time, and the secondary aims were to identify bibliometric performance and influence networks of publications, authors, institutions, and countries. Sensitivity analyses were conducted for 2016-2022, and 2021 time periods. We included 31,687 documents (591,329 references), which aggregated into a well-structured network with credible clustering. Research productivity was split into an early less productive period mainly focusing on safety issues, and a "psychedelic renaissance" after the 1990s. Major trends were identified for hallucinogens/entheogens, entactogens, novel psychoactive substances (NPS), and on dissociative substances. There was a translational evolution from the bench to the bedside, with phase 2 and 3 trials and/or evidence synthesis in particular. The most recent trends concerned NPS, ketamine-associated brain changes, and ayahuasca-assisted psychotherapy. The USA and Canada were the most productive settings for the research overall, and more recently this geographical distribution became more prominent, reflecting legislative context/policy making. A translational evolution of psychedelics has been occurring, that has brought approval of esketamine for depression and will likely lead to approval of additional psychedelics across mental and physical conditions. Toxicology screening tools for NPS are urgently needed, which in turn might follow the same translational evolution of psychedelics in the future.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.111 | 0.204 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".