Bibliographic record
Abstract
Population ageing is a major challenge for European Union (EU) society and economy, particularly for Italy, which is the oldest country in Europe. According to the World Health Organization, two-thirds of European citizens who have reached the retirement age suffer from at least two chronic conditions, with a strong pressure on healthcare systems. Moreover, EU countries already spend, on average, more than a quarter of their gross domestic product on social protection, above all pensions, health and long term care. The current financial crisis is putting a strain on this system. In this context, it becomes increasingly necessary to promote a healthy and independent ageing, by improving outcomes for patients and society while ensuring health systems sustainability. To this purpose a proactive approach to chronic diseases prevention (primary, secondary and tertiary) as well as an integrated healthcare approach and also patients' empowerment are required so as to make daily life more age-friendly. It is also necessary to share health and social best practices, adopt policies really effective against elderly social exclusion and strengthen older people participation in society. A joint effort of all key stakeholders is needed to create a society in which older people can play an active role.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".