The generic version of China Health Related Outcomes Measures (CHROME-G): psychometric testing and comparative performance with the EQ-5D-5L and SF-6Dv2 among the Chinese general population
Bibliographic record
Abstract
OBJECTIVES: The CHROME-G is the first generic preference-based measure developed in China. This study aimed to validate and compare the psychometric properties of the CHROME-G with the EQ-5D-5L and SF-6Dv2 among the Chinese general population. METHODS: A representative sample of the Chinese general population in terms of age, gender, education, and urban/rural residence was recruited for an online survey. During the survey, respondents completed three instruments (first the CHROME-G, then the EQ-5D-5L and SF-6Dv2 in random order), demographic and health-related questions. The retest survey was carried out after two weeks. Ceiling/floor effects were first assessed. Convergent and divergent validity was examined using Spearman's rank correlation. Known-group validity was examined using the non-parametric Kruskal-Wallis H test and effect size. Test-retest reliability was assessed using the intraclass correlation coefficient and weighted Kappa statistics. RESULTS: One thousand respondents (51.1% male, mean age 44.7 years) completed the first survey, with 378 also completing the retest survey. The mean ± SD completion time was 2.03 ± 0.58 min for the CHROME-G, and 1.37 ± 0.54 and 1.13 ± 0.38 min for the EQ-5D-5L and SF-6Dv2. Only the EQ-5D-5L had a ceiling effect of 35.1%. The range of Spearman rank's correlations was 0.45-0.62 for convergent validity and 0.14-0.46 for divergent validity. Among different health subgroups, the effect size for the CHROME-G, EQ-5D-5L and SF-6Dv2 was 1.348-3.416, 1.362-3.325 and 1.097-2.228, respectively. The ICC for test-retest was 0.791 for the CHROME-G, compared with 0.994 and 0.971 for the EQ-5D-5L and SF-6Dv2. CONCLUSIONS: The CHROME-G showed good and comparable psychometric properties with the EQ-5D-5L and SF-6Dv2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".