Norms for the EQ-5D-5L among the general adult population in Alberta, Canada
Bibliographic record
Abstract
PURPOSE: To present EQ-5D-5L population norms for the general adult population in Alberta, Canada. METHODS: We analyzed data from 11 population-based surveys conducted in Alberta between 2012 and 2021. By applying appropriate sampling weights, we estimated normative data for the EQ-5D-5L dimensions, index scores, and visual analogue scale (VAS) scores. This analysis was conducted for the overall population as well as for subgroups categorized by age, sex, provincial health zones, and 17 chronic conditions. RESULTS: The analysis included data from 60,447 respondents (ages 18-99; 51.4% female) across various survey waves, revealing minimal variations in sample characteristics and EQ-5D-5L scores over time. The most frequently reported problems were pain/discomfort (62.2%) and anxiety/depression (41.7%), while 22.5% of respondents reported no issues on any dimension. The mean (SD) EQ-5D-5L index score was 0.845 (0.137), and the mean EQ VAS score was 77.4 (16.7). There was a notable increase in the proportion of reported problems across all dimensions with age, except for anxiety/depression, which showed a decline with advancing age. Females reported slightly more problems across all dimensions compared to males. Individuals with chronic pain had the lowest EQ-5D-5L index scores, followed by those with anxiety and depression, while the lowest EQ VAS scores were observed in individuals with congestive heart failure, kidney disease, and chronic obstructive pulmonary disease. CONCLUSION: This study provides EQ-5D-5L norms for the adult population in Alberta. These reference values can be used to benchmark patients' outcomes as well as to establish burden of illness in this population and facilitate the interpretation of EQ-5D-5L scores in various applications.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".