Developing a pragmatic evaluation of ICTs for older adults with cognitive impairment at scale: the IN LIFE experience
Bibliographic record
Abstract
Abstract Implementing information and communications technology (ICT) at scale requires evaluation processes to capture the impact on users as well as the infrastructure into which it is being introduced. For older adults living with cognitive impairment, this requires evaluation that can accommodate different levels of cognitive impairment, alongside input from family and formal caregivers, plus stakeholder organisations. The European Horizon 2020 project INdependent LIving support Functions for the Elderly (IN LIFE) set out to integrate 17 technologies into a single digital platform for older people living with cognitive impairment plus their families, care providers and stakeholders. The IN LIFE evaluation took place across six national pilot sites to examine a number of variables including impact on the users, user acceptance of the individual services and the overall platform, plus the economic case for the IN LIFE platform. The results confirmed the interest and need among older adults, family caregivers, formal caregivers and stakeholders, for information and communications technology (ICT). Relative to the baseline, quality of life improved and cognition stabilised; however, there was an overall reluctance to pay for the platform. The findings provide insights into existing barriers and challenges for adoption of ICT for older people living with cognitive impairment.
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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 | 0.063 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".