Perspectives of Frailty and Frailty Screening: Protocol for a Collaborative Knowledge Translation Approach and Qualitative Study of Stakeholder Understandings and Experiences
Bibliographic record
Abstract
BACKGROUND: Accompanying the unprecedented growth in the older adult population worldwide is an increase in the prevalence of frailty, an age-related clinical state of increased vulnerability to stressor events. This increased vulnerability results in lower social engagement and quality of life, increased dependency, and higher rates of morbidity, health service utilization and mortality. Early identification of frailty is necessary to guide implementation of interventions to prevent associated functional decline. Consensus is lacking on how to clinically recognize and manage frailty. It is unknown how healthcare providers and healthcare consumers understand and perceive frailty, whether or not they regard frailty as a public health concern; and information on the indirect and direct experiences of consumer and healthcare provider groups towards frailty are markedly limited. METHODS: We will conduct a qualitative study of consumer, practice nurse, general practitioner, emergency department physician, and orthopedic surgeons' perspectives of frailty and frailty screening in metropolitan and non-metropolitan South Australia. We will use tailored combinations of semi-structured interviews and arts-based data collection methods depending on each stakeholder group, followed by inductive and iterative analysis of data using qualitative description. DISCUSSION: Using stakeholder driven approaches to understanding and addressing frailty and frailty screening in context is critical as the prevalence and burden of frailty is likely to increase worldwide. We will use the findings from the Perceptions of Frailty and Frailty Screening study to inform a context-driven identification, implementation and evaluation of a frailty-screening tool; drive awareness, knowledge, and skills development strategies across stakeholder groups; and guide future efforts to embed emerging knowledge about frailty and its management across diverse South Australian contexts using a collaborative knowledge translation approach. Study findings will help achieve a coordinated frailty and healthy ageing strategy with relevance to other jurisdictions in Australia and abroad, and application of the stakeholder driven approach will help illuminate how its applicability to other jurisdictions.
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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 | 0.071 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".