Population-based estimates of different dosage types of psychedelic use across socio-demographic groups in Germany
Bibliographic record
Abstract
Psychedelic drugs, particularly taking small amounts of psychedelics in a cyclical pattern over days (so-called microdosing), have garnered growing scientific and public interest, but representative data on different dosage levels is scarce. To better understand this trend, we surveyed a nationwide sample of 11,299 adults in Germany. The survey assessed lifetime and past six-month psychedelic use by dosage as well as socio-demographic variables (sex, age, education, employment status, household equivalence income, partner arrangements, and place of residence). Results show that 5.0% of respondents self-reported lifetime psychedelic use, while 0.7% reported past six-month use. Medium to high dosing was more prevalent than microdosing. Moreover, high probabilities of using multiple forms of psychedelics were uncovered. We also observed variation in use across socio-demographic groups. For example, psychedelics use was less likely in females than males, and older than younger respondents. Past six-month microdosing was less likely in rural areas, and past six-month medium to high dosing was less prevalent in individuals with higher income or who live with a partner. This study shows limited support for widespread use in Germany and highlights diverse usage patterns across socio-demographics. These findings can inform policies, especially considering the overlap in usage of various substances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".