Sustainable Technologies for Older Adults
Bibliographic record
Abstract
The exponential evolution of technology and the growth of the elderly population are two phenomena that will inevitably interact with increasing frequency in the future. This paper analyses scientific literature as a means of furthering progress in sustainable technology for senior living. We carried out a bibliometric analysis of papers published in this area and compiled by the Web of Science (WOS) and Scopus, examining the main participants and advances in the field from 2000 to the first quarter of 2021. The study describes some interesting research projects addressing three different aspects of older adults’ daily lives—health, daily activities and wellbeing—and policies to promote healthy aging and improve the sustainability of the healthcare system. It also looks at lines of research into transversal characteristics of technology. Our analysis showed that publications mentioning sustainability technologies for older adults have been growing progressively since the 2000s, but that the big increase in the number of research works in this area took place during the period 2016–2021. These more recent works show a tendency to study those factors that improve healthy aging, ensure the social inclusion of the elderly through technology and prolong the time in which they can live independent lives thanks to smart environments. Current research gaps in the literature are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".