Global Drug Survey
Bibliographic record
Abstract
Background: Microdosing psychedelics – the practice of taking small, sub-hallucinogenic doses of substances like LSD or psilocybin-containing mushrooms – is becoming increasingly popular. Despite its surging popularity, little is known about the effects of this practice. Aims: This research had three main aims. First, we attempted to replicate previous findings regarding the subjective benefits and challenges reported for microdosing. Second, we assessed whether people who microdose test their substances for purity before consumption. Third, we examined whether having an approach-intention to microdosing was predictive of more reported benefits. Methods: The Global Drug Survey (GDS) runs the world’s largest drug survey. Participants who reported last year use of LSD or psilocybin in GDS2019 were offered the opportunity to answer a sub-section on microdosing.Results: Data from 6,753 people who reported microdosing at least once in the last 12 months were used for analyses. Our results suggest a partial replication of previously reported benefits and challenges among the present sample often reporting enhanced mood, creativity, focus, and sociability. Counter to our prediction, the most common challenge participants associated with microdosing was “none”. As predicted, most participants reported not testing their substances. Counter to our hypothesis, approach-intention – microdosing in order to approach a desired goal – predicted less rather than more benefits when microdosing. We discuss alternate theoretical frameworks that may better capture the reasons people microdose.Conclusion: Our results suggest that the benefits associated with microdosing greatly outweigh the challenges. Microdosing may have utility for a variety of uses while having minimal side-effects. However, double-blind, placebo-controlled experiments are still required in order to substantiate these reports.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.056 |
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".