A portrait of older adults in naturally occurring retirement communities in Ontario, Canada: A population‐based study
Bibliographic record
Abstract
BACKGROUND: Naturally occurring retirement communities (NORCs) are geographical areas that have naturally become home to a large concentration of older adults. This density means that NORCs have the potential to become a pillar for aging in place strategies, but at present, there is limited data on residents and their health needs. Our objective was to describe and compare the health and healthcare use of older adults living in high-rise NORC buildings to those in all other housing types in the community. METHODS: We conducted a population-based descriptive study of community-dwelling older adults aged ≥65 years by linking a provincial NORC registry in Ontario, Canada with health administrative records. Individuals were classified as NORC residents if their residential postal code on January 1, 2020 matched the NORC registry. Sociodemographic, clinical, and healthcare use characteristics were compared by NORC status using standardized differences (STD) and stratified by rurality, and further by age and sex in urban settings. RESULTS: Overall, 219,995 (7.7%) of 2,869,706 older adults were NORC residents. Compared to community-dwelling older adults, NORC residents were older (mean 77.4 vs 74.6 years; STD 0.34), and more were female (61.8% vs 52.2%; STD 0.19) and had low income (16.0% vs 9.3%; STD 0.11). NORC residents also had more active chronic conditions (mean 1.9 vs 1.5; STD 0.27), medications (mean 3.4 vs 2.8; STD 0.21), home care use (15.3% vs 9.8%; STD 0.17), and primary care visits (mean 9.7 vs 7.6 visits in prior 2 years; STD 0.22). Findings were robust across rurality, age, and sex. CONCLUSIONS: Our findings suggest that NORC residents have greater health needs than other older adults living in the community and underscore NORCs as important targets for equity-focused strategies to support aging in place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".