Guidelines for Healthier Public Spaces for the Elderly Population: Recommendations in the Spanish Context
Bibliographic record
Abstract
A healthy city is more than a place where the population does not suffer from infectious diseases, epidemics, or there is an effective preventive vaccination control, as in the XX century. A healthy European city will be one that can provide physical, social and environmental well-being, to all its inhabitants (WHO. Bulletin of the World Health Organization (BLT) 88(4):241–320, 2010). Many countries already have guides, recommendations or handbooks, such as Canada, Australia, the United States, or the United Kingdom. As the aged population is increasing all over the world, public spaces need to be adapted to the elderly population. The elderly urban population have specific environmental requirements, they experience different levels of thermal comfort in summer and winter, are more sensitive to extreme temperatures, have more difficulties in moving, and lack cognitive sensations, etc. The goal of these guidelines is to establish and analyse the criteria that must be met by urban streets and other public spaces to reduce the environmental health impacts and risks on the elderly population; a threefold action plan is proposed based on safer and walkable streets, nature-based solutions and suitable spaces where they can coexist. The Spanish context is addressed by selective outputs taken into account. The methodology presented can potentially be applied in other countries, considering their particular social, urban and environmental conditions, to deliver active plans for ageing and projects in cities for the elderly population.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".