Bibliographic record
Abstract
OBJECTIVES: This article compares the impact of various self-reported chronic conditions on health-related quality of life, as measured by the Health Utilities Index 3 (HUI3), for the population aged 12 or older. DATA SOURCE: The data are from the cross-sectional household component of the Health file of the 1996/97 National Population Health Survey. ANALYTICAL TECHNIQUES: The effect of 21 chronic conditions was assessed for the full sample (73,402) and in subgroups by age and sex. All analyses were weighted to represent the Canadian population at the time of the survey. The effect of each chronic condition on the HUI3 was estimated using multivariate linear regression, adjusting for age, sex and co-morbidity. MAIN RESULTS: The average impact of different chronic conditions on health status varies substantially. At younger ages, urinary incontinence and arthritis/rheumatism have the greatest effect on health-related quality of life, while at older ages, Alzheimer's disease and the effects of stroke have a major impact. Assessments of the impact of any specific condition should account for the presence of other conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".