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Record W1972385449 · doi:10.3390/ijerph110303032

Use of Neuroenhancement Drugs: Prevalence, Frequency and Use Expectations in Switzerland

2014· article· en· W1972385449 on OpenAlexaff
Stéphane Deline, Stéphanie Baggio, Joseph Studer, Alexandra N’Goran, Marc Dupuis, Yves Henchoz, Meichun Mohler‐Kuo, Jean‐Bernard Daeppen, Gerhard Gmel

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineEnvironmental healthPsychiatryPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study investigates the use expectations, prevalence and frequency of neuroenhancement drug (ND) use among the Swiss male population, separating college students from others. METHODS: Young Swiss men were invited to participate in the Cohort Study on Substance Use Risk Factors. A total of 5,967 participants responded to questions on six types of NDs (wakefulness medication, antidepressants, Alzheimer's disease medication, Parkinson's disease medication, attention deficit-hyperactivity disorder (ADHD) medication, and beta-blockers). The frequency of use depending on five expectations (to enhance wakefulness, attention, memory, concentration and stress reduction) was analyzed for a twelve-month period. RESULTS: (1) About 3% of the sample indicated use of at least one ND; (2) ADHD medication was the most prevalent; (3) The type of ND preferred differed depending on academic status (4). Quantitatively, over the year, college student users used ND much less frequently than other users. CONCLUSIONS: Prevalence of ND use is low in Switzerland relative to other countries such as the United States. Patterns of ND use differed depending on academic status, suggesting that while college student ND users tended to do so rarely (probably to enhance cognitive abilities for exams), non-college male users used other NDs more frequently (probably to "get high").

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.182
GPT teacher head0.410
Teacher spread0.228 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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