The aging population and Natural Occurring Retirement Communities (NORCs); local government, healthy aging, and healthy-NORCs.
Bibliographic record
Abstract
BACKGROUND: The term Naturally Occurring Retirement Communities (NORCs) has been used since the 1980s. NORCs are defined as communities where people remain or move to when they retire. NORCs develop 'naturally', meaning that seniors tend to remain or move there when they retire, although the residences and physical environment were not constructed for a senior population. The term, Healthy-NORC, has been introduced and is associated with healthy aging. OBJECTIVES/METHODS: We describe how demographic trends will facilitate a dramatic growth in NORCs. Acknowledging the 'Determinants of Health' model, we suggest that some determinants impact people differently at different ages. We also suggest that more attention be focused on the impact of physical/social environments on health, and that some determinants of health are particularly relevant for seniors. We argue that NORCs exist on a spectrum, from NORC to H-NORC, and that health benefits for seniors increase as NORCs adopt additional characteristics associated with improved senior health. We also illustrate H-NORC research methods and policy options for local governments. RESULTS/CONCLUSION: Compared to the provision of additional medical and social services, H-NORCs represent a low-cost approach to facilitating healthy aging. Municipal governments can promote healthy aging and should pursue policies that will stimulate H-NORC development.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".