The Mobility Makeover: Exploring the Interest, Need and Feasibility of a Mobility Aid Personalisation Programme in Long-Term Care
Bibliographic record
Abstract
Personalising a new space by bringing furniture and photographs from home can promote a sense of belonging, self-identity and ease a challenging transition from independent living to a care home for older adults. Personalising a mobility device by adding coloured lights or hanging keychains from past travels has found similar benefits for some older adults. To date, research on device personalisation has focussed on community-based older adults, and we know very little about if, and how, this might work for older adults living in long-term care (LTC). The objectives of this study were to: (a) determine interest and support for an assistive device personalisation programme in LTC; (b) understand current processes in device selection, prescription and care; and (c) generate suggestions for implementing a device personalisation programme. Using a qualitative research design, 15 participants (staff, residents and families) from two care homes were interviewed for the study. Findings show support for a device personalisation programme, highlight a system where function is prioritised and personal choice and self-expression are limited and identify challenges and recommendations for implementation. A limitation of the study is that participants were all volunteers and therefore findings may not reflect the full range of perspectives of staff, residents and family members. There are several important implications of this research including identifying the potential benefits of a device personalisation programme and how this might ‘work’ in a care home and providing insight into what may be lost in current systems where the function and efficiency of mobility device prescription are prioritised.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".