P3‐417: Kind Streets: Urban Re‐Design and Ageing in Place with Cognitive Impairment
Bibliographic record
Abstract
Urban planners across the globe are assessing the development of city spaces built around the needs of people, rather than the accommodation of traffic flow. This reassessment coincides with the need to accommodate the large numbers of people choosing the city over the sprawling landscapes of the suburbs. Programs such as the World Health Organization, through the Age-Friendly Cities program (Iwarsson et al. 2007), and the European Union’s ENABLE-Age project (Menec et al. 2013), have highlighted the need for better design of buildings, including homes and enlightened urban design to promote walkable access to amenities including food, libraries, entertainment and medical care through the adaptation of the space to support the needs of elders living with dementia in community settings (Gonyea and Burns, 2013). Literature Review This review considers the needs of older people with neuro-cognitive dysfunction and the options for re-engineered urban social spaces. The identified change in living patterns among Generations X and Y, which is coincidental with the increase in the ageing population, has the potential for a positive impact on the living conditions of older people with cognitive impairment in urban environments – and to reverse the trend of social disconnection resulting from a lack of accessible social spaces within the public realm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".