Modifiable factors associated with frailty in older Australians in retirement living: A partial proportional odds model
Bibliographic record
Abstract
OBJECTIVE: Frailty in older adults is a vulnerable state, often leading to a reduction in function and quality of life. This study sought to identify modifiable factors associated with frailty in Australian retirement village residents. METHODS: A cross-sectional survey was undertaken with individuals 65 years or older living in 25 retirement villages to collect demographic, health and lifestyle information and screen for frailty (modified Reported Edmonton Frail Scale) and loneliness (UCLA 3-item Loneliness Scale). Partial proportional odds modelling was utilised to determine modifiable resident characteristics associated with frailty, accounting for age and gender. RESULTS: Of 2240 residents, 1230 completed the survey (55% response rate) with 1081 eligible for analysis. Respondent frailty levels were as follows: Not Frail = 67% (n = 720), Prefrail = 14% (n = 157), Mildly Frail = 11% (n = 123), Moderately-Severely Frail = 7% (n = 81). For individuals 85-89 years old, age was significantly associated with increased odds of frailty (OR = 3.40; 95% CI: 1.62-7.09; p = .001). After adjusting for age and gender, the odds of higher frailty were greater for individuals experiencing (a) pain, which interfered with usual activities (interferes sometimes OR = 3.17; 95% CI: 2.42-4.15; p ≤ .001; interferes all of the time OR = 10.18; 95% CI: 5.42-19.14; p ≤ .001), or (b) feelings of loneliness (OR = 2.55; 95% CI: 1.80-3.62; p ≤ .001). For Not Frail or Prefrail persons, a recent fall incident was associated with enhanced odds of frailty (OR = 2.60; 95% CI: 1.69-3.98; p ≤ .001). CONCLUSIONS: This cohort of older adults living in Australian retirement villages had greater odds of frailty if experiencing pain, loneliness or falls. Addressing these risk factors could reduce or delay progression to frailty and optimise positive ageing in this population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".