Prevalence of Legal, Prescription, and Illegal Drugs Aiming at Cognitive Enhancement across Sociodemographic Groups in Germany
Bibliographic record
Abstract
There has been speculation about a growing demand for substances used without medical need for cognitive enhancement (CE). Thus, the prevalence rates and the identification of sociodemographic groups at risk of this behavior need further description and constant monitoring. We conducted a nationwide web-based representative sample (N = 22,101) (regarding sex, age, education, and federal state) of the general adult population in Germany. Results show a high past twelve months prevalence of consuming caffeinated drinks for CE (62.4% of respondents), followed by food supplements and home remedies (31.4%), and caffeine tablets (2.5%). The twelve-month prevalence of CE with prescription drugs was 3.7% (lifetime: 5.5%), of whom 29.1% reported using them 40 or more times; 40.5% of all respondents indicated some future intake willingness. Cannabis was the most frequently reported illegal drug for CE (past twelve months: 4.0%; lifetime: 10.7%), followed by the category amphetamine and methamphetamine (past twelve months: 1.0%; lifetime: 2.4%), and cocaine (past twelve months: 0.9; lifetime: 2.4%). We also show variation in the prevalence across multiple ascribed and achieved sociodemographic characteristics. These results can inform public policy and prevention strategies regarding the continued monitoring of the prevalence of CE and the identification of groups at risk of drug misuse.
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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 | 0.001 |
| 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".