AGE-FRIENDLY CITIES AND COMMUNITIES PROGRAM: SUCCESS STORIES IN PARANÁ/BRAZIL
Bibliographic record
Abstract
This article aims at investigating the challenges of population aging both globally and in Brazil, focusing on public policies implemented in the state of Paraná in the southern region of the country. Since 2017, Paraná has been developing initiatives aimed at the elderly population. The Federal Technological University of Paraná (UTFPR), Pato Branco campus, has been leading research and outreach projects related to aging, emphasizing the creation of cities and communities that are welcoming to the elderly. The UTFPR Friendly Team for the Elderly collaborates with the State Secretariat for Women, Racial Equality, and the Elderly in executing the Paraná Friend of the Elderly Program, as well as working with the World Health Organization (WHO) to integrate municipalities into the Global Network of “Age-Friendly Cities and Communities.” The team has developed a comprehensive methodology that includes sociodemographic diagnosis, listening to the elderly population, and creating a municipal action plan, promoting the technical and scientific training of local managers so that their municipalities can obtain international certification from the WHO. Currently, there are 1.685 cities/communities registered in the Global Network, with the highest concentration in the Americas, and the United States, Canada, Chile, Mexico, and Brazil are the leaders. In Brazil, 50 cities are certified as age-friendly, of which 38 are in Paraná, with the support of the UTFPR team. This article aims at outlining the trajectory and advances of Brazilian cities within the WHO Global Network.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".