Frailty screening among older adults receiving home care packages: a study of feasibility and prevalence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Home care packages (HCPs) aim to support older people to remain in their homes for as long as possible. Early detection and management of frailty in community-dwelling older people may prevent or delay transfer to residential aged care. This suggests that it is important to establish mechanisms for identifying frailty among older adults. This study examined the feasibility of obtaining self-reported level of frailty from a sample of older adults receiving HCPs from an Australian aged care provider. The prevalence of frailty and sociodemographic and clinical correlates were assessed. Customers aged =65 years receiving an HCP from an aged care and disability service provider in New South Wales (n = 158; 53.5% consent rate) completed a survey during their scheduled visit. The mean (±s.d.) total score on the Edmonton Frail Scale was 7.3 ± 2.4 (range 1–13). The prevalence of frailty was 45% (5% ‘severe frailty’, 14% ‘moderate frailty’, 26% ‘mild frailty’). Clients who had fallen in the past year had, on average, a 1.0-point higher frailty score (95% confidence interval 0.16–1.90) than those who had not. Given the high proportion of adults in our sample who were identified as frail, regular standardised assessments of frailty may assist community aged care providers to provide early intervention to reduce the risk of falls within this group of clients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it