SOCIAL INTEGRATION IN TEMPORARY HOUSING: PERSPECTIVES OF OLDER PERSONS EXPERIENCING HOMELESSNESS
Bibliographic record
Abstract
Abstract The prevalence of homelessness among older persons in North America is rising. Accordingly, age-supportive housing and service models are needed to adequately support older persons experiencing homelessness to secure permanent housing. Within permanent supportive housing models, achieving and maintaining social integration (i.e., strong social connection and engagement) is recognized as a programmatic goal. Previous research has identified strategies to promote social integration in permanent supportive housing models, such as Housing First models. However, minimal research has considered strategies to promote social integration in temporary housing programs (THPs) and transitional housing models. This study addresses this knowledge gap by examining experiences of social integration, connection, and support in a THP program for older persons experiencing homelessness in Vancouver, Canada. We conducted semi-structured qualitative photovoice interviews with 11 current or former THP clients (Mean age = 65 years; 6 female, 5 male) across three sessions. Data were analyzed using a critical realist-informed thematic analysis method. We identified three themes: 1) technology access and programming can facilitate social connection; 2) consistent communication with program staff can enhance perceived social support; and 3) accessible built environments can promote social participation among clients. Findings offer insights for the development of age-supportive housing and support models for older persons experiencing homelessness. We conclude by offering strategies, such as digital literacy programming and the creation of ‘third spaces’, to support older persons to remain (or become) integrated while in temporary and transitional housing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".