Psychometric validation of the <i>Diagnostic Assessment Research Tool</i>: Alcohol use disorder module
Bibliographic record
Abstract
AIMS: Structured clinical interviewing is considered the gold standard in psychiatric diagnosis. The Diagnostic Assessment Research Tool (DART) is a novel modularized, non-copywritten, 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 to: (i) validate the factor structure of the DART AUD module and (ii) 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 AUD recovery attempt (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 analyses 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). Confirmatory factor analysis results suggested excellent fit for the unidimensional one-factor AUD model (χ2 = 0.09, confirmatory factor index = 0.99, Tucker Lewis index = 0.99, standardized root mean square residual = 0.06, root mean square error of approximation = 0.02). Measurement invariance analyses revealed that the factor structure was equivalent between sex, age, ethnicity, and study site. CONCLUSIONS: Findings provide strong evidence for the psychometric validity of the DART AUD module and support its use in research and clinical practice. The DART represents a credible alternative to other diagnostic interviewing tools for AUD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".