Understanding frailty: a qualitative study of European healthcare policy-makers’ approaches to frailty screening and management
Bibliographic record
Abstract
Objective To elicit European healthcare policy-makers’ views, understanding and attitudes about the implementation of frailty screening and management strategies and responses to stakeholders’ views. Design Thematic analysis of semistructured qualitative interviews. Setting European healthcare policy departments. Participants Seven European healthcare policy-makers representing the European Union (n=2), UK (n=2), Italy (n=1), Spain (n=1) and Poland (n=1). Participants were sourced through professional networks and the European Commission Authentication Service website and were required to be in an active healthcare policy or decision-making role. Results Seven themes were identified. Our findings reveal a ‘knowledge gap’, around frailty andawareness of the malleability of frailty,which has resulted in restrictedownership of frailtyby specialists. Policy-makers emphasised the need to recognise frailty as a clinical syndrome but stressed that it should be managed via an integrated and interdisciplinary response to chronicity and ageing. That is, through social co-production. This would require aculture shift in carewith redeployment of existing resources to deliver frailty management and intervention services. Policy-makers proposedbarriers to a culture shift,indicating a need to be innovative with solutions to empower older adults to optimise their health and well-being, while still fully engaging in the social environment. Thecultural acceptance of an integrated care systemtheme described the complexities of institutional change management, as well as cultural issues relating to working democratically, while insignposting adult care, the need for a personal navigator to help older adults to access appropriate services was proposed. Policy-makers also believed thatscreening for frailtycould be an effective tool for frailty management. Conclusions There is potential for frailty to be managed in a more integrated and person-centred manner, overcoming the challenges associated with niche ownership within the healthcare system. There is also a need to raise its profile and develop a common understanding of its malleability among stakeholders, as well as consistency in how and when it is measured.
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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 | 0.048 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".