Aging in Place:
Bibliographic record
Abstract
While aging in place is preferred by the vast majority of adults and can bring a host of psychological and physical benefits, older adults require community support in order to age in place safely and with dignity.In this commentary, we review the demographic changes and characteristics of older adults nationally and in Delaware, highlight some of the benefits and challenges to aging in place, and discuss the individual and system-level strategies that are needed to help older adults successfully age in place.Finally, we provide an overview of one creative solution that addresses instrumental and social needs among individuals aging in place with chronic illness. Aging in the United StatesThe United States is facing a "gray tsunami" as the baby boomer generation ages and fertility rates decline.In 2010, there were 40.3 million Americans over 65, a number that is expected to grow to 80 million by 2030. 1 Delaware is not isolated from this trend: it is predicted that the Delaware over-65 population will increase 48.6% between 2020 and 2050, from 183,822 to 273,105. 2 For many older adults, managing a chronic disease will be a central part of the aging experience.Nationwide, 78% of adults over 55 have a chronic condition (e.g., arthritis, asthma, cancer, cardiovascular disease, chronic obstructive pulmonary disease, or diabetes), a rate that swells to 85% in adults over 65. 3 In Delaware, four of the top five leading causes of death are chronic diseases.4 Doi: 10.32481/djph.2022.08.007 24.Petersen, J. (2022).A meta-analytic review of the effects of intergenerational programs for youth and older adults.Educational Gerontology, 1-15.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".