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Record W2058705456 · doi:10.1002/hup.883

Patterns and predictors of medication compliance, diversion, and misuse in adult prescribed methylphenidate users

2007· article· en· W2058705456 on OpenAlexafffund
Christine Darredeau, Sean P. Barrett, Bianca Jardin, Robert O. Pihl

Bibliographic record

VenueHuman Psychopharmacology Clinical and Experimental · 2007
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsDalhousie UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMethylphenidateMedical prescriptionStimulantPsychiatryPrescription Drug MisuseMedicineCompliance (psychology)Substance misuseSubstance abuseAttention deficit hyperactivity disorderPsychologyMental healthPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine patterns and predictors of medication compliance, diversion, and misuse in a sample of adults with prescriptions for the stimulant medication methylphenidate (MPH). METHODS: Sixty-six adults currently prescribed MPH (53% male) completed structured interviews and provided details regarding their medication and other substance use histories. RESULTS: On average, participants reported using their medication as prescribed on 14.5 (SD 11.7) of the past 30 days; 44% admitted to diverting it and 29% admitted to inappropriate use. While analyses revealed that medication misuse, diversion, and level of compliance were interrelated and all associated with concurrent illicit substance use, each also had other distinct associations. Specifically, MPH misuse was associated with the use of illicit stimulants such as amphetamine and cocaine, diversion with age and age of MPH prescription, and compliance with participation in an attention deficit hyperactivity disorder (ADHD) support group. Regression analyses revealed that misuse and poor compliance were both best predicted by concurrent illicit substance use, while the model that best predicted diversion included age of first MPH prescription (younger) and MPH misuse. CONCLUSIONS: Poor medication compliance, diversion, and misuse are relatively common and interrelated among adult MPH users. MPH prescriptions should be monitored closely in individuals with histories of illicit substance use.

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)0.000
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 score0.021
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.050
GPT teacher head0.431
Teacher spread0.380 · 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

Citations87
Published2007
Admission routes2
Has abstractyes

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