The Role of the Socio-Physical Environment on Aging in Place for Older Adults in Cohousing and Naturally Occurring Retirement Communities
Bibliographic record
Abstract
The majority of Canada’s older adults want to “age in place” in their home and community as long as possible, even in the face of declining health and physical functioning. Cohousing and Naturally Occurring Retirement Communities (NORC) have been identified as potential aging in place phenomenon. However, empirical research on both communities in Canada is either scarce or nonexistent. A multiple-case study design was used to gain an understanding of the influence of the physical and social environment of residential settings and neighbourhoods on aging in place processes among older adults in cohousing and NORC. Twenty (20) older adults living in cohousing or NORC in British Columbia, Canada were recruited to conduct photovoices and semi-structured interviews. Data was collected and analyzed following constructivist grounded theory methodology. Findings show that aging in place processes were influenced by interacting factors found at multiple levels. At the individual and psychosocial level, aging in place was influenced by older adults’ health status, functional ability, mobility capacity, agency, resilience, and feeling of safety. At the physical environment level, associations with accessibility, functionality, neighbourhood destinations, and aesthetics were found. At the social environment level, aging in place was linked to community engagement, mutual support, meaningful social connections, and the social fabric of the neighbourhood. In addition, mobility was central to participants’ experience of place. Based on these findings, a conceptual framework on aging in place is proposed to better explain the complex dynamics between older adults and the physical and social environments of the neighbourhood. The integrated analysis of the residential and neighbourhood environments highlighted the relevance of considering “place” in aging as a continuum of various geographical scales in future research. This study documents, for the first time in Canada, the experience of older adults living in NORC and cohousing communities. In which manner these communities may provide an optimal environment for aging in place needs to be further documented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".