Making communities age friendly: state and municipal initiatives in Canada and other countries
Bibliographic record
Abstract
To promote healthy, active aging, the age-friendly community initiative has evolved in Canada, Spain, Brazil and Australia, among other countries. An age-friendly community provides accessible and inclusive built and social environments where older adults can enjoy good health, participate actively and live in security. The rapid expansion of the initiative in all states can largely be explained by common key activities undertaken by the state, municipal and -in the case of Canada- also federal, governments. These initiatives include strategic engagements and policy action in all states, and knowledge development and exchange in Canada in particular. Strategic engagements involve creating or strengthening collaborative intersectoral relationships to access multiple arenas of decision-making, and addressing all areas that constitute an age-friendly community. With variations across states, policy actions have included the following: declaring the initiative as an official policy direction; establishing model cities to be emulated by other cities; funding community projects; implementing consistent methodology; evaluating implementation, enhancing public visibility, and aligning age-friendly community policy with other state-level policy directions. To stimulate knowledge development and exchange, Canadian efforts have included the creation of a community of practice and of a research and policy network to encourage the development and translation of scientific evidence on aging-supportive communities. These activities are expected to result in a strong and durable integration of older persons' views, aspirations, rights and needs in municipal, as well as state, planning and policy.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".