Comparing Three Measures of Health Status (Perceived Health With Likert-Type Scale, EQ-5D, and Number of Chronic Conditions) in Chinese and White Canadians
Bibliographic record
Abstract
BACKGROUND: Measures of perceived health status may be vulnerable to ethnic and sociodemographic characteristics. The purpose of this study was to compare self-reported health status in Chinese and whites using 3 measures: physical and mental health status with the 5-point Likert-type scale, the EQ-5D together with a modified health index scale (0-100), and number of chronic conditions. METHODS: A cross-sectional telephone survey of Chinese and white Canadians was conducted in a large city in Alberta, Canada. RESULTS: We analyzed 830 Chinese and 789 white respondents. Chinese, compared with whites, reported better health status using the EQ-5D health index (0.94 vs. 0.86) and had fewer chronic conditions surveyed (51.9% vs. 79.2% had one or more conditions). However, Chinese rated their health status fair or poor more often than whites (27.3% vs. 9.7% for physical health and 24.0% vs. 5.0% for mental health) and both groups rated similarly on the health index scale (80.0 for Chinese vs. 77.9 for white). CONCLUSIONS: Health status measurements performed inconsistently across ethnic populations. The EQ-5D health index was consistent with the number of chronic conditions, whereas results from the 5-point Likert-type scale and the health index scale were not consistent with the number of chronic conditions. Perceived health status differed by the measures used and ethnicity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".