Over one‑third of doctors and a quarter of nurses on average across EU countries were aged over 55 years in 2022
Bibliographic record
Abstract
The 2024 edition of Health at a Glance: Europe examines the major challenges facing European health systems in the aftermath of the COVID-19 pandemic. The report includes two thematic chapters. The first chapter provides a comprehensive examination of health workforce shortages in Europe, a long-standing problem exacerbated by the immense strain the pandemic placed on health systems. It explores the factors behind these shortages and proposes policy strategies to attract, train and retain the workforce needed to build resilient health systems. The second chapter reviews the most recent trends in the health of Europe’s ageing population. With life expectancy continuing to rise and the share of the population over 65 growing steadily, the chapter discusses priorities to promote healthy longevity to reduce demands on health and long-term care systems. The remaining chapters provide a comparative overview of the latest data on health status, risk factors and health system performance across the 27 EU member states, 9 EU candidate countries, 3 European Free Trade Association countries and the United Kingdom. Health at a Glance: Europe 2024 is the first step in the State of Health in the EU cycle.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".