Improving Care for Elders Who Prefer Informal Spaces to Age-Separated Institutions and Health Care Settings
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite advantages of urban areas (such as walkability, public transportation, nearby shopping, and health care services), challenges remain for elders aging in place to access care. The changing demographics of older adults, with higher rates of divorce, singlehood, and childlessness, often living alone and far from family, necessitate new strategies to support health and well-being. RESEARCH DESIGN AND METHODS: Drawing on 5 years of ethnographic fieldwork and 25 interviews with elders in New York City, this study presents empirical insights into older adults' use of "third places" close to home, in conjunction with more formal settings. RESULTS: This article identifies external and internalized ageism and complicated age-based identity as important reasons why older adults preferred "third places" to age-separated spaces such as senior centers and formal settings such as health care settings. We find that neighborhood "third places" offer important physical venues for older adults to process negative or hurried interactions in other formal and age-separated places. DISCUSSION AND IMPLICATIONS: This article makes policy suggestions for increasing access and usage of essential services, including developing attractive and appealing intergenerational spaces in which older community members can obtain services and dispatching caseworkers to public spaces where elders congregate. Furthermore, this article recommends improving exchanges between health care providers and older adults so that they feel recognized, respected, and cared for, which can improve health care 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".