TRANS-DISCIPLINARY RESEARCH IN FRAILTY TO ACHIEVE HEALTHY AGEING
Bibliographic record
Abstract
The benefits of recognizing frailty and addressing the syndrome sooner rather than later range from a healthier older population to reduction in the demand on clinical health services, to a better quality of life for older people, that is more productive, enjoyable activity with more confidence. The National Health and Medical Research Council of Australia has funded a Centre of Research Excellence (CRE) for five years in the health services category to address the ‘silent’ public health issue of frailty. This ‘global’ CRE provides the platform for a diverse team of researchers to collaborate and engage with key stakeholders with the mission of developing and conducting innovative, high quality and trans-disciplinary frailty research with one goal in mind: to prevent and better manage frailty so that people can achieve ‘Healthy Ageing’. This CRE brings together clinician researchers from geriatric medicine, general practice, rehabilitation medicine, orthopaedics, pharmacy, nursing and allied health together with research experts in knowledge translation, economics, demography and geography. The four broad aims of this Frailty CRE are to: a) define the extent of frailty and inform health service policies; b) develop and test a new health economics model for frailty; c) test the implementation of a screening pathway to support early risk identification in general practice; and d) develop and test interventions to treat older people at risk for frailty in the community. As a direct result of our research, we hope to influence the development of innovative and translatable models of care.
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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 | 0.068 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".