Factors associated with transition to a nursing home in older adults living in naturally occurring retirement communities
Bibliographic record
Abstract
BACKGROUND: Naturally occurring retirement communities (NORCs) are geographic areas (generally high-rise buildings or neighborhoods) that have a high concentration of individuals 65 years and older. Supportive service programs in NORCs can address resident needs and delay nursing home (NH) admission but understanding what factors are associated with NORC residents requiring NH admission is needed to tailor such programs. Our aim was to examine individual- and neighborhood-level factors associated with NH wait-list status in NORC residents in Ontario. METHODS: We conducted a population-based, cross-sectional study of Ontario adults 65 years of age or older living in a NORC building as of January 1, 2020, by linking a provincial registry of NORC high-rise buildings with health administrative data. Older adults were classified as being on the NH wait-list if they had an open application for a NH on the index date. We conducted a multilevel logistic regression analysis using generalized estimating equations to determine individual- and neighborhood-level factors associated with NH wait-list status, including sociodemographic, clinical, healthcare use, and building factors. We explored the role of sex and age through stratification by sex (male, female) and age (65-80 and 80+ years). RESULTS: Among 220,864 NORC residents, 4710 individuals (2.1%) were on the NH wait-list. Female sex, older age, immigrant status, dementia diagnosis, receiving homecare, multimorbidity, and polypharmacy (five or more unique drug names) were associated with an increased odds of wait-list status. Several neighborhood-level variables were associated with a significantly increased likelihood of wait-list status, including low income, high dependency, high ethnic diversity, and living in a building with supports. CONCLUSION: NORC supportive service programs can be tailored to account for the factors associated with NH wait-list status, allowing NORC residents who are living in the community to age in their desired place and achieve optimal health outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".