Measuring Health Preferences for Health Utilities Index Mark 3 Health States: A Study of Feasibility and Preference Differences among Ethnic Groups in Singapore
Bibliographic record
Abstract
BACKGROUND: Little is known about the health preferences of Southeast Asians. The authors therefore investigated the feasibility of measuring health preferences of Chinese, Malays, and Indians in Singapore and compared their preference scores. METHODS: A stratified random sample of the Singaporean general population was interviewed to measure preferences for a set of health states defined by the Health Utilities Index Mark 3 (HUI3) using both the rating scale (RS) and the standard gamble (SG) methods. Feasibility of preference measurement was assessed using ratings of measurement tasks, task completion rates, and ranking of preference scores. Differences in preference scores across Chinese, Malays, and Indians were examined using analysis of variance models. RESULTS: Among 245 interviewed respondents (Chinese: 110, Malays: 73, Indians: 62), 97.1% and 95.1% successfully completed all the RS and SG measurement tasks, respectively; 70.1% and 75.3% judged the RS and SG tasks as "easy" or "very easy," respectively. Interviewers rated 69.4% and 75.0% of these respondents as having "full comprehension" for the RS and SG tasks, respectively; "full concentration" was observed in 84.1% and 84.0% of these respondents for the RS and SG tasks, respectively. There were no significant differences in mean preference scores across Chinese, Malays, and Indians, with and without adjustment for effects of confounding variables. CONCLUSIONS: RS and SG are feasible methods for measuring health preferences for Asians in Singapore; it appears that Chinese, Malays, and Indians in Singapore have similar preferences for HUI3 health states.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".