The use of technology in the context of frailty screening and management interventions: a study of stakeholders’ perspectives
Bibliographic record
Abstract
BACKGROUND: Health and social care interventions show promise as a way of managing the progression of frailty in older adults. Information technology could improve the availability of interventions and services for older adults. The views of stakeholders on the acceptability of technological solutions for frailty screening and management have not been explored. METHODS: Focus groups were used to gather data from healthy and frail/pre-frail older adults, health and social care providers, and caregivers in three European countries - Italy, Poland and UK. Data were analysed using framework analysis in terms of facilitators or determinants of older adults' adoption of technology. RESULTS: Our findings clustered around the perceived value; usability, affordability and accessibility; and emotional benefits of frailty screening and management technology to stakeholders. We also noted issues relating to social support, previous experience of technology and confidence of stakeholders. CONCLUSIONS: Professionals and caregivers understand the benefits of technology to facilitate frailty care pathways but these views are tempered by concerns around social isolation. Frail older adults raised legitimate concerns about the accessibility and usability of technology, specifically around the potential for their personal information to be compromised. Solutions must be developed within a framework that addresses social contexts and avoids stigma around frailty and ageing.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".