Social connectedness of older adults living in affordable rental housing
Bibliographic record
Abstract
Housing affordability is identified as a major barrier to meeting the core housing needs of older adults in Canada. Metro Vancouver in British Columbia (BC), Canada, is known for its lack of adequate supply of affordable rental housing and high rental rates. Older adult renters in these markets face many challenges, one of which is the lack of social connectedness. Social connectedness is defined as a positive subjective evaluation of the extent to which one has meaningful, close, and constructive relationships with others, and which plays a vital role in older adults’ health and well-being. It is found that social connectedness allows older adults to develop meaningful connections with others and reduces their risk of experiencing loneliness. However, recent evidence shows that older adults globally are experiencing a decline in social connectedness. Older adults living in affordable rental housing are disproportionately impacted by this decline as they are less likely to have the resources to accommodate for the associated losses. Literature on this topic is limited, especially in the context of affordable rental housing, as research has predominantly focused on older adults living in settings such as long-term care facilities, nursing homes or privately owned homes. To address this gap in the literature and work towards supporting the needs of this growing population, this project presents a research grant proposal based on the Real Estate Foundation of BC’s (REFBC) ‘General Grants’ application guidelines. This proposal explores the factors that impact the social connectedness of older adults living in affordable rental housing and outlines a mixed methods research approach to addressing the particular needs of this population. Older adults living in eight to ten purposefully selected affordable rental housing buildings in the Greater Vancouver area of BC, will be invited to participate in photo-voice and semi-structured interviews to explore their social- interactions and connectedness. The photo-voice and semi-structured interviews will be complemented by researcher-led built- and social environment observations and survey data to provide a holistic understanding of older adults’ experiences in this area.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".