Stability of normative data for the SF-36: results of a three-year prospective study in middle-aged Canadians.
Bibliographic record
Abstract
BACKGROUND: The SF-36 is widely used to assess health-related quality of life (HRQOL), but with few longitudinal studies in healthy populations, it is difficult to quantify its natural history. This is important because any measure of change following an intervention may be confounded by natural changes in HRQOL. This paper assesses mean changes in SF-36 scores over a 3-year period in men and women between the ages of 40 and 59 years at baseline. METHODS: Subjects were randomly selected from nine Canadian cities. Mean SF-36 changes over a 3-year period (1996/1997-1999/2000) were calculated for each gender within 5-year age categories. Multiple imputation was used to correct for potential bias due to missing data. RESULTS: The baseline cohort included 1,974 women and 975 men between 40 and 59 years. Mean changes in HRQOL tended to be small. Women demonstrated small average declines in 22 of the 32 age and domain groupings (4 age groups, 8 SF-36 domains) while men showed declines in 14/32. Most participants stayed within 10 points of their original baseline score. INTERPRETATION: Mean SF-36 scores change only slightly over three years in middle-aged Canadians, although there is much individual variation. It may be necessary to adjust for the natural evolution of SF-36 scores when interpreting results from longitudinal studies.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".