‘Aging in the Right Place’: An Analysis of the Demographic Profile and Residential Relocation Patterns of Older Adults Living in Residential Facilities in Kingston, Ontario
Bibliographic record
Abstract
Despite the firm policy push for Canadian older adults (65+) to continue aging in their own homes, relocating to a residential facility (RF) continues to be both a preferred and needs-driven decision for some older adults. RFs serve as a key factor in supporting the growing older population, bridging the gap between one’s home and long-term care facilities. Understanding why some older adults choose to relocate to RFs and their geographic residential relocation patterns is critical to ensuring that these facilities can effectively support the needs and preferences of this population. Applying the concept of ‘aging in the right place’, this thesis takes an exploratory approach to understanding the demographic profile of older adults living in RFs in Kingston, Ontario, and the geographic patterns and what influences their residential relocations. Both quantitative and qualitative analyses were performed on the questionnaire responses gathered at four RFs in Kingston (n=41). Findings on the demographic profile of the participants aligned with the literature that older adults living in RFs are healthier, wealthier, better educated, and of a White racial background. Results of the quantitative analysis revealed that the majority of participants had relocated from within Kingston, thus continuing to age in place in their community. Gender appeared to influence whether one had undergone a health-related relocation, with results of the binary logistic regression revealing that males are more likely to experience a health-related relocation than a non-health-related relocation. Applying the push and pull factor model, individuals who rated their general health as fair, those who did not own their previous dwelling, and those who previously lived outside of Kingston were all more likely to have relocated because of a pull factor. Content analysis revealed three overarching themes pertaining to the most-liked features of RF living and four themes relevant to the least-liked features. This research highlights the existing need and desire for RFs as an option for older adults to age in the right place. The results of this exploratory research call for future research on the residential relocation patterns and demographic profile of residents living in RFs in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".