The impact of frailty and cognitive impairment on quality of life: employment and social context matter
Bibliographic record
Abstract
ABSTRACTBackground:How cognitive impairment and frailty combine to impact on older adults' Quality of Life (QoL) is little studied, but their inter-relationships are important given how often they co-occur. We sought to examine how frailty and cognitive impairment, as well as changes in frailty and cognition, are associated with QoL and how these relationships differ based on employment status and social circumstances. METHODS: Using the Survey of Health, Ageing, and Retirement in Europe data, we employed moderated regression, followed by simple slopes analysis, to examine how the relationships between levels of health (i.e., of frailty and cognition) and QoL varied as a function of sex, age, education, social vulnerability, and employment status. We used the same analysis to test whether the relationships between changes in health (over two years) and QoL varied based on these same moderators. RESULTS: Worse frailty (b = -1.61, p < .001) and cognitive impairment (b = -0.08, p < .05) were each associated with lower QoL. Increase in frailty (b = -2.17, p < .001) and cognitive impairment (b = -0.25, p < .001) were associated with lower QoL. The strength of these relationships varied depending on interactions with age, sex, education, social vulnerability, and employment status. Higher social vulnerability was consistently associated with lower QoL in analyses examining both static health (b = -3.16, p < .001) and change in health (b = -0.66, p < .001). CONCLUSIONS: Many predictors of QoL are modifiable, providing potential targets to improve older adults' QoL. Even so, the relationships between health, cognition, and social circumstances that shape QoL in older adults are complex, highlighting the importance for individualized interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".