Comparative performance of the EuroQol EQ-5D-5L and the CDC healthy days measures in assessing population health
Bibliographic record
Abstract
OBJECTIVES: To examine the comparative performance of EuroQol EQ-5D-5L and Center for Disease Control Healthy Days measures in assessing population health. METHODS: Using data from 2014 Alberta Community Health Survey, a cross-sectional population-based survey (N = 7559), conducted in Alberta, Canada, we examined construct validity of the measures as indicators of population health. Differences in EQ-5D-5L index score, visual analogue scale (EQ-VAS), and CDC unhealthy days index across socio-demographic subgroups were tested by Mann-Whitney and Kruskal-Wallis tests using known-groups approach. RESULTS: EQ-5D-5L and CDC Healthy Days provided comparable assessments of population health in this sample. Both measures discriminated between subgroups defined by self-perceived health status, level of education, and material deprivation. The discriminative ability of CDC Healthy Days was limited in capturing variability in health among age groups compared to the EQ-5D-5L. Among participants who reported 0 unhealthy days, the proportion of those with level 3 problems in pain/discomfort varied from 1.1% for participants aged 18-24 to 19.2% for those over 75 years. CONCLUSIONS: EQ-5D-5L demonstrated better construct validity than CDC Healthy Days in assessing health in a population-based sample of adults.
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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 | 0.039 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".