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Record W820979917

Network of Cities Tackles Age-Old Problems: Jane Parry Investigates How Cities around the World Are Catering for the Explosive Growth in the Number of People Aged over 60

2010· article· en· W820979917 on OpenAlexaboutno aff
Jane Parry

Bibliographic record

VenueBulletin of the World Health Organization · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)UrbanizationEconomic growthPopulationPacePARRYChecklistSocioeconomicsSociologyGeographyPsychologyDemography
DOInot available

Abstract

fetched live from OpenAlex

The world's population of people aged over 60 will double from 11% in 2006 to 22% by 2050. Even more dramatic will be the growth in the number of the very old. Between 1950 and 2050, the number of people over the of 80 will grow from 14 million to 400 million worldwide. At the same time, the pace of urbanization continues unabated: by 2030 an estimated three in five people will be urban dwellers. All levels of government are starting to realize how important the demographic transition says John Beard, director of the Department of Ageing and Life Course at the World Health Organization (WHO) in Geneva. People have been struggling for a number of years in knowing how to respond. WHO's Age-friendly programme gives them something tangible as a way to address these trends. The programme encourages leaders in cities as diverse as New York and Nairobi to think constructively about how to improve life for older people, a process that has the potential to enhance city life for everyone. To this end, in 2006, representatives from 33 cities in 22 countries met and examined eight areas where cities might influence healthy ageing: outdoor spaces and buildings, transportation, housing, social participation, respect and social inclusion, civic participation and employment, communication and information, and community support and services. [ILLUSTRATION OMITTED] Following the meeting, a guide and checklist were produced for cities to use to assess their age friendliness. Involving older people in assessing age-friendliness and identifying measurable indicators to demonstrate progress ensures it's not just a feel-good exercise says Beard. The next step was the establishment of the Global Network of Age-friendly Cities, which connects participating cities from around the world. The network gives member cities access to technical support and training from WHO, as well as the opportunity to share information and experiences. When a city joins the network, it commits to an initial five-year programme. In the first phase it needs to establish mechanisms that involve older people; conduct an assessment of the city's age-friendliness; develop an action plan and measures that will show if it is making a difference. It then has three years to implement its plan and demonstrate its progress. If the city wishes to stay in the network, it must show continuous improvement through cycles of implementation and evaluation. A distinguishing feature of the network's approach is the way that it extends far beyond the traditional sector. We see healthy ageing as being inextricably linked to an individual's social context, rather than social context being just a factor that affects health says Beard. Remaining socially engaged is just as important a component of an older person's as the absence of diabetes. We're hoping to get information through the network on what cities have learnt, what are best practices, what challenges they faced and whether or not they found solutions explains Simone Powell from WHO's Department of Ageing and Life Course. Cities can serve as role models and show that some of the achievements may not cost a lot. Unsurprisingly, many measures that make a city age-friendly also make it friendlier for other groups; outdoor seating, accessible public toilets and pedestrian crossings timed with slow walkers in mind also benefit pregnant women, carers of small children and people with disabilities. The programme team has been contacted by many additional cities that are now initiating age-friendly city projects, such as Donostia-San Sebastian in Spain and Berne in Switzerland. In other countries, national initiatives are emerging. France, for example, has 30 cities signed up to its National Programme on Ageing. The concept has taken off in Canada and Ireland and WHO is also talking to China's National Committee on Ageing about a scheme that might help more than 400 million Chinese citizens who will be aged over 60 by 2050. …

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.009
Open science0.0030.007
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0310.010

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.

Opus teacher head0.033
GPT teacher head0.324
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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