Investigating the built environment surrounding naturally occurring retirement communities (NORCs) in Toronto
Bibliographic record
Abstract
Introduction A positive built environment can improve older adult health and support aging in place, yet little is known about those surrounding Naturally Occurring Retirement Communities (NORCs), geographic areas where many older adults live. We investigated walkability and amenity density surrounding NORCs in Toronto, Canada. Methods This population-based descriptive study linked built environment datasets with health administrative data and a provincial registry of high-rise NORC buildings by postal code. Mean walkability index scores, quartiles, as well as amenity density categories, were compared for NORC and non-NORC postal codes, or “sites”. Older adult resident characteristics were compared for NORC sites in the least and most favourable categories for each outcome. Results Our analysis of walkability and amenity density was based on 49,295 (488 [9.9 %] NORC) and 49,945 (489 [9.8 %] NORC) Toronto postal codes. NORC sites were in more walkable neighbourhoods (mean walkability 6.4 (SD 8.2) versus 4.3 (7.4) for non-NORC sites, std 0.26); although, 55 (11.3 %) were in the lowest quartile of neighbourhood walkability in Toronto. NORC sites were also in more amenity dense neighbourhoods, with 63.0 % in medium/high density neighbourhoods compared to 50.5 % of non-NORC sites, std 0.25. NORC residents in the least walkable or amenity-poor neighbourhoods were older, and proportionately more were immigrants compared to NORC residents in the most walkable and amenity-rich neighbourhoods. Conclusions Findings suggest that built environments surrounding high-rise NORC buildings 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".