Quality of life of the Canadian population using the VR-12: population norms for health utility values, summary component scores and domain scores
Bibliographic record
Abstract
OBJECTIVES: To estimate Canadian population norms (health utility values, summary component scores and domain scores) for the VR-12. METHODS: English and French speaking Canadians aged 18 and older completed an online survey that included sociodemographic questions and standardized health status instruments, including the VR-12. Responses to the VR-12 were summarized as: (i) a health utility value; (ii) mental and physical component summary scores (MCS and PCS, respectively), and (iii) eight domain scores. Norms were calculated for the full sample and by gender, age group, and province/territory (univariate), and for several multivariate stratifications (e.g., age group and gender). Results were summarized using descriptive statistics, including number of respondents, mean and standard deviation (SD), median and percentiles (25th and 75th), and minimum and maximum. RESULTS: A total of 6761 people who clicked on the survey link completed the survey (83.4% completion rate), of whom 6741 (99.7%) were included in the analysis. The mean health utility score was 0.698 (SD = 0.216). Mean health utility scores tended to be higher in older age groups, ranging from 0.661 (SD = 0.214) in those aged 18-29 to 0.728 (SD = 0.310) in those aged 80+. Average MCS scores were higher in older age groups, while PCS scores were lower. Females consistently reported lower mean health utility values, summary component scores and domain scores compared with males. CONCLUSIONS: This is the first study to present Canadian norms for the VR-12. Health utility norms can serve as a valuable input for Canadian economic models, while summary component and domain norms can help interpret routinely-collected data.
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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 | 0.004 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".