Canadian normative data for the SF-36 health survey. Canadian Multicentre Osteoporosis Study Research Group.
Bibliographic record
Abstract
BACKGROUND: The Medical Outcomes Study 36-item Short Form (SF-36) is a widely used measure of health-related quality of life. Normative data are the key to determining whether a group or an individual scores above or below the average for their country, age or sex. Published norms for the SF-36 exist for other countries but have not been previously published for Canada. METHODS: The Canadian Multicentre Osteoporosis Study is a prospective cohort study involving 9423 randomly selected Canadian men and women aged 25 years or more living in the community. The sample was drawn within a 50-km radius of 9 Canadian cities, and the information collected included the SF-36 as a measure of health-related quality of life. This provided a unique opportunity to develop age- and sex-adjusted normative data for the Canadian population. RESULTS: Canadian men scored substantially higher than women on all 8 domains and the 2 summary component scales of the SF-36. Canadians scored higher than their US counterparts on all SF-36 domains and both summary component scales and scored higher than their UK counterparts on 4 domains, although many of the differences are not large. INTERPRETATION: The differences in the SF-36 scores between age groups, sexes and countries confirm that these Canadian norms are necessary for comparative purposes. The data will be useful for assessing the health status of the general population and of patient populations, and the effect of interventions on health-related quality of life.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".