Technologies and applications for active and assisted living-current situation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The world is addressing significant challenges due to the current and future demographic contexts. The number of people aged 65 years or over in Europe and the United States will almost double between 2015 and 2060 [1, 2]. This will be linked with an increase in people requiring long term care, i.e. a continuum of medical and social services designed to support the needs of people living with chronic health problems that affect their ability to perform everyday activities [3]. Currently, approximately 30% of people between 65 and 80 years of age require long-term care. This percentage reaches 50% for those over 80 [4]. Longevity of people combined with the decline in birth rate will also put pressure on the economic support of this care. The Statistical Office of the European Communities (EUROSTAT) projects that, in the next 30 years, the ratio between working and retired people, i.e. the old age support rate, will move from four-to-one to two-to-one in the EU [5]. Nowadays, EU Member States spend approximately a quarter of their GDP on social protection [6]. These demographic and economic situations raise significant challenges towards health and social care of the older population in terms of increased costs and lack of resources.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it