Healthy Naturally Occurring Retirement Communities: The Need for Increased Collaboration Between Local Public Health Agencies and Municipal Government
Bibliographic record
Abstract
Naturally occurring retirement communities (NORCs) exist on a “health promoting” continuum in the extent to which they facilitate healthy aging. Some NORCs are healthier than others for seniors because their physical and social environments encourage activity and promote feelings of well-being. Municipal governments and public health agencies have different potential roles in developing healthy-NORCs. Municipal government responsibilities affect housing, transportation, green space, and zoning policies, which in turn affect the physical and built environment, a key senior-sensitive determinant of health. Public health agency responsibilities include population-based approaches to health promotion and chronic disease and injuries prevention through the encouragement of behaviors such as healthy eating and physical activity. Public health recognizes the importance of supportive environments, to which the built environment contributes. The gap between the responsibilities of public health and those of municipal government hinders the development of healthy-NORCs. Public health, although responsible for health promotion, has limited ability to influence the built environment. The municipal government is responsible for policy affecting the built environment, but health promotion is rarely considered in this exercise. Public policy aimed at facilitating healthy aging would be supported by increased collaboration between public health and municipal government.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".