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Record W2765414741 · doi:10.1177/1087054717714058

The Qualitative Interview Study of Persistent and Nonpersistent Substance Use in the MTA: Sample Characteristics, Frequent Use, and Reasons for Use

2017· article· en· W2765414741 on OpenAlexaff
James M. Swanson, Timothy Wigal, Peter S. Jensen, John T. Mitchell, Thomas S. Weisner, Desiree W. Murray, L. Eugene Arnold, Lily Hechtman, Brooke S. G. Molina, Elizabeth B. Owens, Stephen P. Hinshaw, Katherine A. Belendiuk, Sharon B. Wigal, Page Sorensen, Annamarie Stehli

Bibliographic record

VenueJournal of Attention Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsCarleton UniversityMcGill University Health Centre
FundersNational Institute on Drug AbuseNational Institute of Mental Health
KeywordsPsychologyClinical psychologySubstance useQualitative researchPerceptionPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate participants' perceptions about frequent use and reasons for substance use (SU) in the qualitative interview study, an add-on to the multimodal treatment study of ADHD (MTA). METHOD: Using the longitudinal MTA database, 39 ADHD cases and 19 peers with Persistent SU, and 86 ADHD cases and 39 peers without Persistent SU were identified and recruited. In adulthood, an open-ended interview was administered, and SU excerpts were indexed and classified to create subtopics (frequent use and reasons for use of alcohol, marijuana, and other drugs). RESULTS: For marijuana, the Persistent compared with Nonpersistent SU group had a significantly higher percentage of participants describing frequent use and giving reasons for use, and the ADHD group compared with the group of peers had a significantly higher percentage giving "stability" as a reason for use. CONCLUSION: Motivations for persistent marijuana use may differ for adults with and without a history of ADHD.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.186
GPT teacher head0.397
Teacher spread0.211 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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