Developing age-friendly communities: practice and policy initiatives from across the world
Bibliographic record
Abstract
In the face of global challenges associated with population ageing, social exclusion and increasingly complex urban environments, there is an urgent need to understand how to make communities more age friendly. More specifically, there is a growing recognition that older people themselves have a key role to play in ensuring that their communities enable people to age well. This session brings together international researchers from a number of countries with an interest in exploring the relationship between well-being in later life and the social and physical environment in which people live. Rationale for choice of participants: In recent years, research across the world has begun to explore how to engage older people in identifying the factors that lead to age-friendly communities. Common themes in the research include a focus on the engagement of older people, valuing the ‘voice’ of older people, working in local communities/ neighbourhoods, and working collaboratively with local government and organisations involved with older people. This symposium brings together a group of these researchers from Canada, the UK, Australia and the USA, who are all working within their own policy contexts and with local seniors to develop local age-friendly environments. This session is aimed at reviewing the findings from different projects, identifying commonalities, and exploring strategies for future research. Novelty and scientific interest in the international research context: While the World Health Organization’s Age-friendly Cities Project (2006-2007) provided a high-profile demonstration of how urban environments are an important factor mediating the experiences and opportunities open to older people, it also demonstrated the effectiveness of a bottom-up, participatory approach. Panelists draw together this and other research that shows that empowerment of older people to work with others in their communities is an important way of optimizing their opportunities for health, participation and security as they age. The session builds on insights from complementary research in a variety of locations to explore how older people work with others to build age-friendly environments.
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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 | 0.100 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.021 | 0.039 |
| Open science | 0.008 | 0.071 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".