The Australian Frailty Network: Development of a consumer‐focussed national response to frailty
Bibliographic record
Abstract
Frailty is an important concept in the care of older adults. Over the past two decades, significant advances have been made in measuring frailty. While it is now well-recognised that frailty status is an important determinant of outcomes from medical illnesses or surgical interventions, frailty measurement is not currently routinely integrated into clinical practice. In the community setting, it is uncommon for general practitioners to deliver frailty-optimised care. In hospitals, there is substantial variability in how people living with frailty are managed. This variability is notable between and even within disciplines. Furthermore, gains from understanding frailty mechanisms and risk factors are not yet applied/implemented at scale to delay the progression of frailty in community-dwellers. The Australian Frailty Network (AFN) is a national collaborative group of researchers, clinicians, non-government organisations, consumers and policymakers, in which the engagement and active involvement of consumers has been embedded from the outset. The AFN aims to generate new knowledge to improve health outcomes, to ensure evidence-based management is translated into clinical practice and to build capacity in multidisciplinary and translational frailty research. Here, we describe the development of the AFN, highlighting important milestones: (i) securing funding for the network and flagship elements; (ii) an inaugural summit to establish the strategic vision, values and scope with end-users; (iii) sabbatical visits to learn from international examples; and (iv) developing the governance structure and an actionable plan encompassing consumer engagement, research, education and policy and practice to maximise impact.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".