Factor Structure of The Malay-Version Generalized Anxiety Disorder-7 (GAD-7) Questionnaire among Patients with Diabetes Mellitus
Bibliographic record
Abstract
The Malay-version Generalised Anxiety Disorder-7 (GAD-7) questionnaire previously demonstrated good concurrent validity, i.e. sensitivity and specificity as a screening instrument for anxiety. However, its psychometric properties on factorial validity had not been further investigated. This study investigated the factor structure of the Malay-version GAD-7 in among 300 diabetic outpatients (mean age: 60.4 years, SD: 13.4 years; 52.7% male) in a Malaysian university hospital in Kuala Lumpur. Study participants completed questionnaires on sociodemographic information, the GAD-7, the Beck’s Depression Inventory (BDI), and the WHOQOL-BREF instrument. The Malay-version GAD-7 displayed good internal consistency (Cronbach’s α=0.91) and satisfactory convergent validity with depression (Pearson’s R=0.642, p 1 (eigenvalue=4.614), suggesting a unidimensional factor structure. All seven items were loaded on a higher-order factor (‘generalized anxiety’) in confirmatory factor analysis. This model did not have a good fit with the data. After examining the modification indices, the model was respecified to allow covariance of the error terms of items 1 and 2, and 2 and 3. The respecified model appeared to fit the data better (χ2=35.216, df=12, p<0.001, CFI=0.98, TLI=0.97, RMSEA=0.08, and AIC=67.22). The findings suggested that items 1, 2 and 3 of GAD-7 may share distinctive variance out of that explained by the ‘generalized anxiety’ factor. Overall, the Malay-version GAD-7 appeared to be a valid measurement of the symptoms of anxiety in this study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".