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Record W4410851175 · doi:10.1038/s41598-025-03873-0

Population-based estimates of different dosage types of psychedelic use across socio-demographic groups in Germany

2025· article· en· W4410851175 on OpenAlexaff
Sebastian Sattler, Suzanne Wood, Margit Anne Petersen, Fiona Seiffert, Guido Mehlkop

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of TorontoMontreal Clinical Research Institute
FundersUniversität BielefeldDeutsche Forschungsgemeinschaft
KeywordsResidenceDemographyDemographicsDosingRural areaPopulationMedicineGerontologyPsychologySociologyPharmacology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.354
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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