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Record W2120154366 · doi:10.1089/cap.2015.0099

Nonmedical Use of Attention-Deficit/Hyperactivity Disorder Medication Among Secondary School Students in The Netherlands

2015· article· en· W2120154366 on OpenAlexaboutno aff
Ellen S. Koster, Lydia de Haan, Marcel L. Bouvy, Eibert R. Heerdink

Bibliographic record

VenueJournal of Child and Adolescent Psychopharmacology · 2015
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsAttention deficit hyperactivity disorderMedical prescriptionPsychiatryMedicineSelf-medicationAttention deficitPopulationFamily medicineClinical psychologyPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: No studies in Europe have assessed the extent of nonmedical attention-deficit/hyperactivitiy disorder (ADHD) medication use among adolescents, while also, in Europe, prescribing of these medicines has increased. Our objective was to study the prevalence and motives for nonmedical ADHD medication use among secondary school students in the Netherlands. METHODS: Adolescent students 10-19 years of age from six secondary schools were invited to complete an online survey on use of ADHD medication, tobacco, alcohol, and drugs. Nonmedical ADHD medication use was defined as self-reported use without a prescription during the previous 12 months. RESULTS: Survey data were available for 777 students (15% response rate). The overall proportion of students self-reporting nonmedical ADHD medication use was 1.2% (n = 9), which represented almost 20% of the adolescents who reported ADHD medication use (n = 49). Most adolescents reported self-medication or enhancing study performance as motives for ADHD medication use. CONCLUSIONS: The proportion of the study sample reporting nonmedical ADHD medication use in our study is lower compared with that in previous research conducted in the United States and Canada; however, on a population-based level, there might be a considerable proportion of recreational users.

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 score0.010
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.353
Teacher spread0.323 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Child and Adolescent PsychopharmacologySame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207