Psychedelic microdosing benefits and challenges: an empirical codebook
Bibliographic record
Abstract
BACKGROUND: Microdosing psychedelics is the practice of consuming very low, sub-hallucinogenic doses of a psychedelic substance, such as lysergic acid diethylamide (LSD) or psilocybin-containing mushrooms. According to media reports, microdosing has grown in popularity, yet the scientific literature contains minimal research on this practice. There has been limited reporting on adverse events associated with microdosing, and the experiences of microdosers in community samples have not been categorized. METHODS: In the present study, we develop a codebook of microdosing benefits and challenges (MDBC) based on the qualitative reports of a real-world sample of 278 microdosers. RESULTS: We describe novel findings, both in terms of beneficial outcomes, such as improved mood (26.6%) and focus (14.8%), and in terms of challenging outcomes, such as physiological discomfort (18.0%) and increased anxiety (6.7%). We also show parallels between benefits and drawbacks and discuss the implications of these results. We probe for substance-dependent differences, finding that psilocybin-only users report the benefits of microdosing were more important than other users report. CONCLUSIONS: These mixed-methods results help summarize and frame the experiences reported by an active microdosing community as high-potential avenues for future scientific research. The MDBC taxonomy reported here informs future research, leveraging participant reports to distil the highest-potential intervention targets so research funding can be efficiently allocated. Microdosing research complements the full-dose literature as clinical treatments are developed and neuropharmacological mechanisms are sought. This framework aims to inform researchers and clinicians as experimental microdosing research begins in earnest in the years to come.
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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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".