Developing Age-Friendly Cities and Communities: Eleven Case Studies from around the World
Bibliographic record
Abstract
Developing age-friendly cities and communities has become a key part of policies aimed at improving the quality of life of older people in urban areas. The World Health Organization has been especially important in driving the 'age-friendly' agenda, notably through its Global Network of Age-Friendly Cities and Communities, connecting 1114 (2020 figure) cities and communities worldwide. Despite the expansion and achievements of the Network over the last decade, little is known about the progress made by cities developing this work around the world. This article addresses this research gap by comparing the experience of eleven cities located in eleven countries. Using a multiple case study approach, the study explores the key goals, achievements, and challenges faced by local age-friendly programs and identifies four priorities the age-friendly movement should consider to further its development: (1) changing the perception of older age; (2) involving key actors in age-friendly efforts; (3) responding to the (diverse) needs of older people; and (4) improving the planning and delivery of age-friendly programs. The article concludes by discussing the research and policy implications of these findings for the age-friendly movement.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".