The Meaning of "Aging in Place" to Older People
Bibliographic record
Abstract
PURPOSE: This study illuminates the concept of "aging in place" in terms of functional, symbolic, and emotional attachments and meanings of homes, neighbourhoods, and communities. It investigates how older people understand the meaning of "aging in place," a term widely used in aging policy and research but underexplored with older people themselves. DESIGN AND METHODS: Older people (n = 121), ranging in age from 56 to 92 years, participated in focus groups and interviews in 2 case study communities of similar size in Aotearoa New Zealand, both with high ratings on deprivation indices. The question, "What is the ideal place to grow older?" was explored, including reflections on aging in place. Thematic and narrative analyses on the meaning of aging in place are presented in this paper. RESULTS: Older people want choices about where and how they age in place. "Aging in place" was seen as an advantage in terms of a sense of attachment or connection and feelings of security and familiarity in relation to both homes and communities. Aging in place related to a sense of identity both through independence and autonomy and through caring relationships and roles in the places people live. IMPLICATIONS: Aging in place operates in multiple interacting ways, which need to be taken into account in both policy and research. The meanings of aging in place for older people have pragmatic implications beyond internal "feel good" aspects and operate interactively far beyond the "home" or housing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".