Pilot implementation of the mod‐REFS frailty screening tool in an Australian home care provider to improve client health and well‐being
Bibliographic record
Abstract
OBJECTIVE: Frailty refers to increased vulnerability and decreased resilience with associated increased risk of adverse health outcomes. Frailty mostly affects older adults; hence, early identification is necessary to prevent further decline. To help optimise health and well-being, we aimed to implement a holistic frailty screening tool, the modified Reported Edmonton Frail Scale (mod-REFS) within an Australian aged and community home care provider. METHODS: The Implementation Framework for Aged Care guided implementation and evaluation, including co-design with key stakeholders. Clinical (nurses and allied health) and non-clinical (personal care workers) home care staff administered the mod-REFS in a pilot. Evaluation of feasibility, acceptability and fidelity of the tool was undertaken using administrative data alongside a staff survey. RESULTS: Between July and October 2023, the mod-REFS was completed for 218 clients from Queensland and Victoria, with almost two-thirds (n = 142, 65%) identified as either prefrail (n = 57, 26%) or frail (n = 85, 39%). A greater percentage were prefrail and frail in Victoria than in Queensland. The staff survey (n = 27) identified that the mod-REFS was considered helpful by most (n = 15, 55%); quick, easy to use, concise, very practical; and able to identify frailty levels and other important issues such as depression. Most staff (n = 23, 85%) required no training to use the tool. CONCLUSIONS: Implementing the mod-REFS to identify prefrailty or frailty was feasible and acceptable when administered by a range of home care staff. Implementation requires input from all stakeholders. Early identification and intervention could prevent deterioration and improve well-being of those receiving home care services.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".