Evaluation of measurement properties of the Health Assessment Questionnaire-Disability Index (HAQ-DI) among gout patients in China
Bibliographic record
Abstract
OBJECTIVES: To evaluate measurement properties of Chinese version of the Health Assessment Questionnaire Disability Index (HAQ-DI) among Chinese gout patients. METHOD: A representative sample of Chinese gout patients were recruited with stratification based on age, sex, urban/rural residence, and education level. Ceiling and floor effects were evaluated. Reliability was assessed by internal consistency (Cronbach’s α). Structural validity was verified by confirmatory factor analysis (CFA). Convergent validity was assessed using Spearman’s rank coefficient, examining the correlation between the HAQ-DI and EQ-5D-5L. Known-groups validity was evaluated by determining the HAQ-DI score differences between subgroup patients. Effect sizes were then used to assess sensitivity of the subgroup differences. RESULTS: A total of 1,000 patients were included in the study. Ceiling and floor effects were both not observed. Cronbach’s α was 0.95. The factor loadings of CFA were all above 0.6 and the model fit indices were acceptable (χ2/df = 5.97, RMSEA = 0.071, RFI = 0.906, CFI = 0.940, TLI = 0.920), indicating that the eight-factor model had well structural validity. The HAQ-DI correlated in predictable ways with five EQ-5D-5L dimensions, with Spearman’s rank coefficient ranging from 0.30 to 0.61. The HAQ-DI can discriminate between subgroup patients with different levels of health status, with the mean effect size (0.73) at a medium level. CONCLUSIONS: Chinese version of the HAQ-DI was verified to have satisfactory reliability, validity, and sensitivity in measuring health-related quality of life of Chinese gout patients. We recommend supplementing the responsiveness of the HAQ-DI to changes over time in future research.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".