Validation of the Chinese Version of KIDSCREEN-10 Quality of Life Questionnaire: A Rasch Model Estimation
Bibliographic record
Abstract
The KIDSCREEN-10 was deemed as a cross-national instrument for measuring Health-Related Quality of Life (HRQoL). However, no empirical endeavor has explored its reliability and validity in the context of China. This study aims to translate and validate the Chinese version of the KIDSCREEN-10 questionnaire. The KIDSCREEN-10 was translated into Chinese (Mandarin) using a blindly bilingual forward-backward-forward technique. A cross-sectional survey, including 1,830 students aged from 8 to 18 years, was conducted in a county located in Gansu province, China. Psychometric properties were evaluated using the Rasch partial credit model, ANOVA, and the correlation analysis. Results indicated that the KIDSCREEN-10 performed good internal consistency, known-group validity, and concurrent validity, but there were still some deficiencies in psychometrics: first, disordered response categories were found between category 2 (seldom) and category 3 (sometimes); second, item 3 ("Have you felt sad?"), item 4 ("Have you felt lonely?"), and item 5 ("Have enough time for self?") demonstrated misfit to the Rasch model; third, items 3 and 4 exhibited differential item functioning. After collapsing the disordered response categories and removing the three misfit items, the seven-item questionnaire performed good psychometric properties. However, the seven-item version does not cover the psychological well-being dimension of HRQoL, and that may lead to inappropriate measures of HRQoL. Therefore, this paper suggested to use classical test theory to investigate the psychological properties of the KIDSCREEN-10.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".