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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".