The Qualitative Interview Study of Persistent and Nonpersistent Substance Use in the MTA: Sample Characteristics, Frequent Use, and Reasons for Use
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".