Item reduction and validation of the Chinese version of diabetes quality-of-life measure (DQOL)
Bibliographic record
Abstract
BACKGROUND: The Diabetes Quality-of-Life (DQOL) Measure is a 46-item diabetes-specific quality of life instrument. The original English version of the DQOL has been translated into Chinese after cultural adaption, and the Chinese DQOL has been validated in the Chinese diabetic patient population and used in diabetes-related studies. There are two recognized problems with the Chinese DQOL: 1) the instrument is too long, and 2) the non-response rate of certain items is relatively high. This study aimed to develop and validate a short version for the Chinese DQOL. METHODS: Item reduction was conducted based on the classical test theory (CTT) and item response theory (IRT), each combined with exploratory factor analysis (EFA). The confirmatory factor analysis (CFA) and Spearman correlation coefficient were employed in validating the short versions. RESULTS: Both the study sample (n = 2,886) and the validation sample (n = 2,286) were from a longitudinal observation study of Chinese type 2 diabetic patients. The CTT kept 32 items, and the IRT kept 24 items from the original 46-item version. The two short versions were comparable in psychometric properties. CONCLUSION: The 24-item IRT-based short version of the Chinese DQOL was selected as the preferred short version because it imposes a lower burden on patients without compromising the psychometric properties of the instrument.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".