Towards Global Age-Friendly Cities: Determining Urban Features that Promote Active Aging
Bibliographic record
Abstract
At the same time as cities are growing, their share of older residents is increasing. To engage and assist cities to become more "age-friendly," the World Health Organization (WHO) prepared the Global Age-Friendly Cities Guide and a companion "Checklist of Essential Features of Age-Friendly Cities". In collaboration with partners in 35 cities from developed and developing countries, WHO determined the features of age-friendly cities in eight domains of urban life: outdoor spaces and buildings; transportation; housing; social participation; respect and social inclusion; civic participation and employment; communication and information; and community support and health services. In 33 cities, partners conducted 158 focus groups with persons aged 60 years and older from lower- and middle-income areas of a locally defined geographic area (n = 1,485). Additional focus groups were held in most sites with caregivers of older persons (n = 250 caregivers) and with service providers from the public, voluntary, and commercial sectors (n = 515). No systematic differences in focus group themes were noted between cities in developed and developing countries, although the positive, age-friendly features were more numerous in cities in developed countries. Physical accessibility, service proximity, security, affordability, and inclusiveness were important characteristics everywhere. Based on the recurring issues, a set of core features of an age-friendly city was identified. The Global Age-Friendly Cities Guide and companion "Checklist of Essential Features of Age-Friendly Cities" released by WHO serve as reference for other communities to assess their age readiness and plan change.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".