Technologies and applications for active and assisted living-current situation
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.034 |
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".