Aging-in-Place in a Mid-sized Canadian City: A Case Study of the Housing Experiences of Seniors in Kelowna's Housing Market
Bibliographic record
Abstract
Kelowna, a mid-size city in the interior of the Okanagan Valley, is one of the fastest growing cities in Canada with one of the most expensive real estate markets. Despite Kelowna’s high proportion of seniors, little has been published about the housing experiences of its senior homeowners and renters. This study focuses on the main housing barriers seniors face, their coping strategies, and the benefits of aging-in-place. The data were obtained from a survey of 79 seniors (aged 65 or older) who are residents of Kelowna (45 homeowners and 34 renters), and semi-structured interviews with 12 key informants who are familiar with housing and seniors’ issues in Kelowna. The evidence indicates that most of the senior homeowners and renters are happy living in the city of Kelowna, find it a good place to retire and a safe place to live, with enough opportunities to meet other seniors and make friends. While most of them aspire to age-in-place, they face significant problems, mostly due to housing affordability and/or accessibility issues. To make aging-in-place more feasible, the seniors and key informants called for more senior government support in the form of affordable housing, as well as policy initiatives for future housing development in Kelowna to accommodate the housing and service needs of its growing senior population. Keywords: seniors; affordable housing; aging-in-place; mid-size city; Kelowna
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".