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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".