Being your best: Protocol for a feasibility study of a codesigned approach to reduce symptoms of frailty in people aged 65 years or more after transition from hospital
Bibliographic record
Abstract
Introduction The population is ageing, with increasing health and supportive care needs. For older people, complex chronic health conditions and frailty can lead to a cascade of repeated hospitalisations and further decline. Existing solutions are fragmented and not person centred. The proposed Being Your Best programme integrates care across hospital and community settings to address symptoms of frailty. Methods and analysis A multicentre pragmatic mixed methods study aiming to recruit 80 community-dwelling patients aged ≥65 years recently discharged from hospital. Being Your Best is a codesigned 6-month programme that provides referral and linkage with existing services comprising four modules to prevent or mitigate symptoms of physical, nutritional, cognitive and social frailty. Feasibility will be assessed in terms of recruitment, acceptability of the intervention to participants and level of retention in the programme. Changes in frailty (Modified Reported Edmonton Frail Scale), cognition (Mini-Mental State Examination), functional ability (Barthel and Lawton), loneliness (University of California Los Angeles Loneliness Scale-3 items) and nutrition (Malnutrition Screening Tool) will also be measured at 6 and 12 months. Ethics and dissemination The study has received approval from Monash Health Human Research Ethics Committee (RES-19-0000904L). Results will be disseminated through peer-reviewed journals, conference and seminar presentations.
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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 | 0.078 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.019 |
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".