Are symbols useful and culturally acceptable in health-state valuation studies? An exploratory study in a multi-ethnic Asian population
Bibliographic record
Abstract
Hwee-Lin, Wee1, Shu-Chuen Li2, Xu-Hao Zhang1, Feng Xie3, David Feeny4, Nan Luo5, Yin-Bun Cheung6, David Machin7, Kok-Yong Fong8, Julian Thumboo81Department of Pharmacy, National University of Singapore (NUS), Singapore; 2Discipline of Pharmacy and Experimental Pharmacology, School of Biomedical Sciences, Faculty of Health, University of Newcastle, Newcastle, NSW, Australia; 3McMaster University, Hamilton, Ontario, Canada; 4Kaiser Permanente Northwest Center for Health Research, Portland, OR, USA; 5Department of Community, Occupational and Family Medicine, Yong Loo Lin School of Medicine, NUS, Singapore; 6Clinical Trials and Epidemiology Research Unit, Singapore; 7School of Health and Related Research, University of Sheffield, Sheffield, UK; 8Department of Rheumatology and Immunology, Singapore General Hospital, SingaporeBackground: Symbols have been used in health state valuation studies to help subjects distinguish the severity of various characteristics of a given health state. Symbols used in such studies need to be evaluated for their cross-cultural appropriateness because a given symbol may have different meanings or acceptability in different cultures, which may affect results of such studies.Objectives: To evaluate if using symbols to differentiate health states of different severity is useful and culturally acceptable in a multi-ethnic, urban Asian population.Methods: Using in-depth interviews with adult Chinese, Malay, and Indian Singaporeans conducted in English/mother-tongue, subjects were shown a health state with 6 levels (Health Utilities Index 3 vision), each displayed with a symbol, and asked (1a) if symbols were useful in differentiating severity of each level (measured using dichotomous and 0–10 visual analog scale [VAS] scales) or (1b) offensive and (2) to assess 7 alternative sets of symbols.Results: Of 63 subjects (91% response rate), 18 (29%) felt symbols were useful in differentiating severity of each level. Reported usefulness of symbols was fair (median VAS score: 3.0, score exceeding 5.0 for 33% of subjects). One Malay subject felt symbols were offensive.Conclusions: Use of symbols for health state valuation was culturally acceptable and useful for some subjects.Keywords: Asian, Southeastern, culture, health status, questionnaires, Singapore
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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 | 0.038 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".