Assisted living technologies and the consumer market: how is it developing?
Bibliographic record
Abstract
Purpose A three-year research study, funded by Innovate UK, Consumer Models for Assisted Living (COMODAL) aimed to support the development of the consumer market for electronic assisted living technology (eALT) products and services, particularly for people aged 50-70, approaching older age and retirement themselves or with caring responsibilities for family or friends. The purpose of the COMODAL study was to gain a greater understanding of their needs and behaviours relating to the acquisition of eALT and develop sustainable consumer-led business models that might address these needs and support business development within a consumer market (Ward et al., 2016). The purpose of this paper is to present a follow up study to explore how the market may have changed since the publication of the research findings. Design/methodology/approach An online survey was used to collect both qualitative and quantitative data from individuals working in the supply and distribution of assisted living technologies in the UK regarding how their businesses had developed in the past two years. Findings The results showed that since the publication of the COMODAL research there have been changes in the way that the consumer market for eALT is being approached, not only with more direct marketing focused on consumer’s needs but also in direct partnerships with local authorities that offer greater choice with an improved range of products. Originality/value This is the first paper in the UK to follow up the impact of the original COMODAL research and explore its influence on the development of the consumer market for eALT.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".