Identification and engagement of naturally occurring retirement communities to support healthy aging in Canada: A set of methods for replication
Bibliographic record
Abstract
BACKGROUND: Naturally occurring retirement communities (NORCs), unplanned communities with a high proportion of older adult residents, offer a model to support older adults to age well in place. The aim of this paper is to provide a comprehensive description of the methods used to identify and engage NORCs appropriate for the development of supportive service programming in Canada. METHODS: Three steps were used to identify and select NORCs in which to develop supportive service programming including: 1) identification of potential NORCs using Canadian Census Dissemination Areas, the Ontario Marginalization Index and Google Maps, 2) engagement of property owner/manager to determine the availability of common space for communal programming and willingness of the owner to support programming and, 3) engagement of older adult residents within the NORC to co-design programming. RESULTS: Four cities in the south-east, south-central, and south-west of Ontario, Canada were identified to develop NORCs with supportive service programming. Using the methods described, six NORCs were identified, landlords and older adult residents were engaged, and programs initiated between April 2018 and March 2019. The sites included two private high-rise apartments, a city-owned low-rise subsidized apartment complex, two multi-building private high-rise complexes and a mobile home community. An average of 35 (min 20, max 78) older adult members were engaged in an average of 20.5 unique activity sessions at each site per month. On average, social (54%) and physical activities (30%) were more common than nutritional (10%) and knowledge-sharing (8%). CONCLUSIONS: The increased prevalence of unplanned, geographically-bound NORCs creates an opportunity for governments, social and health service providers and policy makers to support healthy aging in their communities. Our experience with the creation of six new NORCs with supportive service programming provides a tested set of methods that can be applied in other communities.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".