Psychometric validation of the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9) in an inpatient substance use disorder treatment program.
Bibliographic record
Abstract
OBJECTIVE: The Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) are two widely used instruments for assessing anxiety and depression, respectively, but no studies have examined their psychometric properties among individuals with substance use disorders. This study's objectives were to (a) validate the factor structures, examining single and two-factor models, and (b) examine measurement invariance across age and sex. METHOD: = 41 years) completed the GAD-7 and PHQ-9 as part of routine measurement-based care at admission. Confirmatory factor analysis assessed one-factor and two-factor latent models for the GAD-7 and PHQ-9. RESULTS: Confirmatory factor analysis revealed that in both cases, the one-factor structures exhibited a moderately good fit, with acceptable values for two of four fit indices, but the two-factor structure (with item clusters reflecting cognitive and somatic features) met acceptable fit for all indices. The two-factor models were also invariant across age (examined using quartiles) and sex (female, male). CONCLUSIONS: These findings generally support the psychometric validity of the GAD-7 and PHQ-9 in patients with substance use disorders, but particularly a two-factor model that separates cognitive from somatic features. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".