Frailty Levels in Residential Aged Care Facilities Measured Using the Frailty Index and <scp>FRAIL</scp> ‐ <scp>NH</scp> Scale
Bibliographic record
Abstract
OBJECTIVES: To compare the FRAIL-NH scale with the Frailty Index in assessing frailty in residential aged care facilities. DESIGN: Cross-sectional. SETTING: Six Australian residential aged care facilities. PARTICIPANTS: Individuals aged 65 and older (N = 383, mean aged 87.5 ± 6.2, 77.5% female). MEASUREMENTS: Frailty was assessed using the 66-item Frailty Index and the FRAIL-NH scale. Other measures examined were dementia diagnosis, level of care, resident satisfaction with care, nurse-reported resident quality of life, neuropsychiatric symptoms, and professional caregiver burden. RESULTS: The FRAIL-NH scale was significantly associated with the Frailty Index (correlation coefficient = 0.81, P < .001). Based on the Frailty Index, 60.8% of participants were categorized as frail and 24.4% as most frail. Based on the FRAIL-NH, 37.5% of participants were classified as frail and 35.9% as most frail. Women were assessed as being frailer than men using both tools (P = .006 for FI; P = .03 for FRAIL-NH). Frailty Index levels were higher in participants aged 95 and older (0.39 ± 0.13) than in those aged younger than 85 (0.33 ± 0.13; P = .008) and in participants born outside Australia (0.38 ± 0.13) than in those born in Australia (0.34 ± 0.13; P = .01). Both frailty tools were associated with most characteristics that would indicate higher care needs, with the Frailty Index having stronger associations with all of these measures. CONCLUSION: The FRAIL-NH scale is a simple and practical method to screen for frailty in residential aged care facilities.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".