Age-friendly Environments: Baseline Assessment in Latin America
Bibliographic record
Abstract
Consolidating age-friendly environments is a goal of the Decade of Healthy Ageing (2021–2030). Under the World Health Organization (WHO) Age-Friendly Cities and Communities Framework the first step is to carry out a baseline assessment, with the active participation of older people, in order to determine the areas in which cities and communities must work to remove the barriers experienced by older people and create increasingly friendly environments adapted to their requirements. The WHO program recommends using the Vancouver Protocol to conduct this assessment. Due to particular complications, many Latin American countries have adapted it for local implementation in order to overcome difficulties that arose. Outlining the current knowledge available in Latin America and noting the experiences of cities and communities in the subregion, this document compiles examples and case studies of these adaptations, such as the program implemented in Costa Rica, which will guide policy actions that foster people's full development throughout the life course. In order to respond to the challenges posed by demographic transitions, it is essential to create tools that allow environments to be adapted in ways that promote healthy ageing. This requires accurate, up-to-date, and effective information. The Decade of Healthy Ageing establishes a period of focused action aimed at producing and monitoring information. This is the strategy that serves as the framework for this report.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".