Healthy Ageing: Raising Awareness of Inequalities, Determinants, and What Could Be Done to Improve Health Equity
Bibliographic record
Abstract
PURPOSE OF THE STUDY: Social and scientific discourses on healthy ageing and on health equity are increasingly available, yet from a global perspective limited conceptual and analytical work connecting both has been published. This review was done to inform the WHO World Report on Ageing and Health and to inform and encourage further work addressing both healthy aging and equity. DESIGN AND METHODS: We conducted an extensive literature review on the overlap between both topics, privileging publications from 2005 onward, from low-, middle-, and high-income countries. We also reviewed evidence generated around the WHO Commission on Social Determinants of Health, applicable to ageing and health across the life course. RESULTS: Based on data from 194 countries, we highlight differences in older adults' health and consider three issues: First, multilevel factors that contribute to differences in healthy ageing, across contexts; second, policies or potential entry points for action that could serve to reduce unfair differences (health inequities); and third, new research areas to address the cause of persistent inequities and gaps in evidence on what can be done to increase healthy ageing and health equity. IMPLICATIONS: Each of these areas warrant in depth analysis and synthesis, whereas this article presents an overview for further consideration and action.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".