Psychometric properties of the Generalized Anxiety Disorder Inventory in a Canadian sample
Bibliographic record
Abstract
The Generalized Anxiety Disorder Inventory is a recently developed self-report measure that assesses symptoms of generalized anxiety disorder. Its psychometric properties have not been investigated further since its original development. The current study investigated its psychometric properties in a Canadian student/community sample. Exploratory principal component analysis replicated the original three-component structure. The total scale and subscales demonstrated excellent internal consistency reliability (α = 0.84-0.94) and correlated strongly with the Penn State Worry Questionnaire (r = 0.41-0.74, all ps <0.001) and Generalized Anxiety Disorder-7 (r = 0.55-0.84, all ps <0.001). However, only the total scale and cognitive subscale (r = 0.48-0.49, all ps <0.05) significantly predicted generalized anxiety disorder diagnosis established by diagnostic interview. The somatic subscale in particular may require revision to improve predictive validity. Revision may also be necessary given changes in required somatic symptoms for generalized anxiety disorder diagnostic criteria in more recent versions of the Diagnostic and Statistical Manual of Mental Disorders (i.e. although major changes occurred from Diagnostic and Statistical Manual of Mental Disorders-III-R to Diagnostic and Statistical Manual of Mental Disorders-IV, changes in Diagnostic and Statistical Manual of Mental Disorders-5 were minimal) and the possibility of changes in the upcoming 11th revision of the International Classification of Diseases.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".