INVESTIGATING THE BUILT ENVIRONMENT SURROUNDING NATURALLY OCCURRING RETIREMENT COMMUNITIES (NORCS) IN TORONTO
Bibliographic record
Abstract
Abstract A positive built environment can improve the health of older adults and support aging in place by increasing access to amenities, physical activity, and social interaction. We investigated the walkability and amenity density surrounding Naturally Occurring Retirement Communities (NORCs) in Toronto, areas where many older adults live. This population-based descriptive study linked two built environment datasets with a provincial registry of high-rise NORC buildings in Ontario, Canada by postal code (PC). Mean (SD) walkability index scores and quartiles, as well as amenity density categories were compared for NORC and non-NORC PCs. Building and resident characteristics were also compared for NORC PCs in the least and most favourable categories for each outcome. Our analysis of walkability and amenity density included 49,295 (488 [9.9%] NORC) and 49,945 (489 [9.8%] NORC) Toronto PCs respectively. Walkability was significantly higher for NORC (mean 7.1 (SD 9.1)) versus non-NORC (4.9 (8.3)) PCs; although 57 (11.7%) NORC PCs were in low walkability neighbourhoods. NORC PCs were also more amenity dense, with 63.0% being classified as medium/high density compared to 50.5% of non-NORC PCs. Low walkability and amenity-poor NORCs tended to house older, immigrant, ethnically diverse and lower-income populations. Low walkability NORCs were also more likely to be apartments with supports and have residents without access to basic needs. Overall, our findings suggest that many NORCs are well-positioned to support aging in place given their walkability and amenity access, however action should be taken to support NORCs with suboptimal environment conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".