Life-space, frailty, and health-related quality of life
Bibliographic record
Abstract
INTRODUCTION: Life-space and frailty are closely linked to health-related quality of life and understanding their inter-relationship could indicate potential intervention targets for improving quality of life. We set out to examine the relationship between frailty and life-space and their relative impact on quality of life measures. METHODS: Using cross-sectional data from a population-representative cohort of people aged ≥ 70 years, we assessed quality of life with the EuroQol Health Index tool (5-levels) (EQ-5D-5L). We also undertook a life-space assessment and derived a frailty index. Linear regression models estimated EQ-5D-5L scores (dependent variable) using life-space assessment, frailty index and interactions between them. All models were adjusted by age, sex, lifestyle, and social care factors. RESULTS: A higher EQ-5D Index was associated with higher life-space (0.02 per life-space assessment score, 95%CI: 0.01 to 0.03, p < 0.01) and decreasing frailty (-0.1 per SD, 95%CI: -0.1 to -0.1, p < 0.01). There was evidence of an interaction between life-space and frailty, where the steepest gradient for life-space and EQ-5D was in those with the highest frailty (interaction term = 0.02 per SD of frailty, 95%CI: 0.01 to 0.03, p < 0.01). CONCLUSION: Individuals with the highest frailty were twice as likely to have higher quality of life in association with a larger life-space. Interventions designed to improve quality of life in frail older people could focus on increasing a person's life-space.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".