Randomized Response Estimates for the 12‐Month Prevalence of Cognitive‐Enhancing Drug Use in University Students
Bibliographic record
Abstract
STUDY OBJECTIVE: To estimate the 12-month prevalence of cognitive-enhancing drug use. DESIGN: Paper-and-pencil questionnaire that used the randomized response technique. SETTING: University in Mainz, Germany. PARTICIPANTS: A total of 2569 university students who completed the questionnaire. MEASUREMENTS AND MAIN RESULTS: An anonymous, specialized questionnaire that used the randomized response technique was distributed to students at the beginning of classes and was collected afterward. From the responses, we calculated the prevalence of students taking drugs only to improve their cognitive performance and not to treat underlying mental disorders such as attention-deficit-hyperactivity disorder, depression, and sleep disorders. The estimated 12-month prevalence of using cognitive-enhancing drugs was 20%. Prevalence varied by sex (male 23.7%, female 17.0%), field of study (highest in students studying sports-related fields, 25.4%), and semester (first semester 24.3%, beyond first semester 16.7%). To our knowledge, this is the first time that the randomized response technique has been used to survey students about cognitive-enhancing drug use. CONCLUSION: Using the randomized response technique, our questionnaire provided data that showed a high 12-month prevalence of cognitive-enhancing drug use in German university students. Our study suggests that other direct survey techniques have underestimated the use of these drugs. Drug prevention programs need to be established at universities to address this issue.
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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 | 0.158 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".