Do Rural Residents in China Understand EQ-5D-5L as Intended? Evidence From a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The 5-level EQ-5D (EQ-5D-5L) has been increasingly used in China to measure the health status of the general population and patients. However, its content validity among rural residents in China has not been formally evaluated. This qualitative study aims to assess the content validity of EQ-5D-5L among rural Chinese. METHODS: Participants were recruited from four regions (North, South, East and West) across China. Eligible participants were those living in the rural area in last three years and making a living by agricultural operations. Semi-structured interviews were conducted. Interview transcripts were analysed to assess the comprehensibility, relevance, clarity and comprehensiveness. RESULTS: Sixty-two participants were included, comparable to the national figures regarding age, sex and education. For comprehensibility, participants could understand the 'mobility', 'self-care' and 'usual activities' domains well, but some reported confusions in 'pain/discomfort' (n = 42) and 'anxiety/depression' (n = 35). Some also reported difficulties in understanding anxiety (n = 6) and depression (n = 9), possibly due to the formal wording used. For relevance, all domains were reported as health-related and participants' responses were based on their own health. For clarity, all could distinguish the five levels, but suggestions on reducing response levels and alternative wording for 'slight' were raised. For comprehensiveness, two aspects (fatigue/energy and appetite) were raised beyond the EQ-5D-5L domains. The 'mobility' domain was selected as the most important and 'anxiety/depression' as the least important. CONCLUSION: Rural Chinese reported problems on the content validity of Chinese EQ-5D-5L. It might be sensible to consider some revisions to make it more understandable for rural residents.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".