Examining the Measurement Invariance and Psychometrics of the Drug Abuse Screening Test for Adolescents (DAST-A) in Justice-Involved Youth
Bibliographic record
Abstract
Substance abuse is a serious mental health concern and reoffense risk factor for justice-involved youth. The Drug Abuse Screening Test for Adolescents (DAST-A) is used to assess drug abuse in different contexts, yet its psychometric properties have not yet been thoroughly explored in youth justice samples. We examined the measurement invariance and psychometrics of the DAST-A in a diverse sample of 741 justice-involved youth ( N young men = 636). The tool showed strong reliability in the overall sample and subgroups (ω = .88–.94), and good convergent and concurrent validity. Logistic regression results indicated that, with each unit increase in DAST-A score, the odds of an substance use disorder (SUD) diagnosis increased by 23% (overall sample). The predictive validity findings were more robust for White youth than Black youth and as a result, a different cut-off score was explored for Black youth. The DAST-A demonstrated measurement invariance across gender and race. Practice implications are discussed.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".