One‑third of all doctors and a quarter of nurses in OECD countries were over 55 years of age
Bibliographic record
Abstract
Society at a Glance 2024: OECD Social Indicators, the tenth edition of the biennial OECD overview of social indicators, addresses the growing demand for quantitative evidence on social well-being and its trends. The report features a special chapter on fertility trends which discusses evidence from recent OECD analysis on the effect of labour market outcomes, housing costs and different aspects of the family policy framework (e.g. parental leave, childcare, and financial supports) on fertility trends and highlights key policy challenges. This edition of Society at a Glance also includes a special section based on the 2022 OECD Risks that Matter Survey on people’s perceptions of social and economic risks and the extent to which they think governments address those risks effectively. Society at a Glance presents 25 social indicators, 5 each in chapters on General context, Self-sufficiency, Equity, Health, and Social cohesion. These indicators include data for 38 OECD member countries and, where available, accession and key partners countries (Argentina, Bulgaria, Brazil, Croatia, China, India, Indonesia, Peru, Romania, and South Africa) and another other G20 country (Saudi Arabia).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".