Psychometric Validation of the Diagnostic Assessment for Research and Treatment for Alcohol Use Disorder
Bibliographic record
Abstract
Aims: Structured clinical interviewing is considered the gold standard in psychiatric diagnosis. The Diagnostic Assessment and Research Tool (DART) is a novel modularized semi-structured interview; however, no studies have examined the psychometric properties of its alcohol use disorder (AUD) module. The primary aims of this study were: (1) to validate the factor structure of the DART AUD module and (2) to examine measurement invariance across several key demographic and subgroup factors.Methods: Participants were community members in Hamilton, Canada and Boston, USA who self-identified as making a significant recovery attempt from problematic alcohol use (N = 499). Internal reliability was examined via the Kuder-Richardson 20 statistic, and correlations between symptom count and drinking quantity/frequency were examined. Then, symptom-level data were included in a confirmatory factor analysis to examine model fit of a single hypothesized factor structure. Finally, measurement invariance of the factor were conducted for sex, age, ethnicity (White vs. racialized), and study site. Results: This study found evidence for adequate internal reliability (rKR20 = 0.75), and symptom scores correlated with drinking quantity and frequency (r = 0.16 - 0.43). CFA suggested indices of the unidimensional one-factor AUD model were excellent (χ2 = 0.092, CFI = 0.99, TLI = 0.99, SRMR = 0.06, RMSEA = 0.02). Measurement invariance analyses revealed that the factor structure was equivalent between sex, age, ethnicity, and study site. Conclusions: Together, these findings provide strong evidence for psychometric properties of the AUD DART module and provide psychometric evidence supporting its use in research and clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".