Macrodosing to microdosing with psychedelics: Clinical, social, and cultural perspectives
Bibliographic record
Abstract
To date, the clinical and scientific literature has best documented the effects of classical psychedelics, such as lysergic acid diethylamide (LSD), psilocybin, and dimethyltryptamine (DMT), in typical quantities most often associated with macrodosing. More recently, however, microdosing with psychedelics has emerged as a social trend and nascent therapeutic intervention. This variation in psychedelic practice refers to repeat, intermittent ingestion of less-than-macrodose amounts that do not cause the effects associated with full-blown "trips". Microdosing paves the road to incorporating psychedelic drugs into a daily routine while maintaining, or even improving, cognitive and mental function. Unlike macrodosing with psychedelics, the influence of microdosing remains mostly unexplored. And yet, despite the paucity of formal studies, many informal accounts propose that microdosing plays an important role as both a therapeutic intervention (e.g., in mental disorders) and enhancement tool (e.g., recreationally-to boost creativity, improve cognition, and drive personal growth). In response to this relatively new practice, we provide an integrative synthesis of the clinical, social, and cultural dimensions of microdosing. We describe some of the overarching context that explains why this practice is increasingly in vogue, unpack potential benefits and risks, and comment on sociocultural implications. In addition, this article considers the effects that macro- and microdoses have on behavior and psychopathology in light of their dosage characteristics and contexts of use.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".