CULTIVATING BELONGINGNESS: IN TEMPORARY SUPPORTIVE HOUSING FOR OLDER MALE VETERANS IN CALGARY, CANADA
Bibliographic record
Abstract
Abstract Veterans are two to three times more likely to experience homelessness compared to the general population, with an estimated 2,400 – 10,000+ homeless veterans in Canada. Veterans tend to be older, male, and more likely to cite an illness or medical condition as a contributing factor to their homelessness, than their non-veteran counterparts and thus have distinctly different needs. As part of the Aging in the Right Place (AIRP) partnership, a tri-city multi-methods study, we sought to understand the housing and support needs of older male military veterans (age 50+) living in a congregate temporary supportive housing program (a tiny home barracks), identified as a promising practice, in Calgary, Canada. In this presentation we draw on in-depth qualitative key informant interviews with service providers (n=5) and photovoice interviews with older shelter residents (n=10) to understand the shelter needs of older homeless veterans. Interviews were transcribed, managed with NVivo 1.6.1, and team-based flexible coding was employed on the interviews to determine older male veterans’ shelter needs. Thematic analysis of service provider interviews identified key features to promote AIRP included: program specific challenges and strengths and contexts, while eligibility and systemic barriers were noted as impediments to AIRP. Residents highlighted: autonomy and belonging, community and connection, ageism, routine and daily rhythm, safety, recognition, and the built environment as key to their (in)ability to AIRP in this promising practice. In this presentation we contextualize the study findings and offer recommendations for shelter and services designed to assist older housing insecure veterans to AIRP.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 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) | 0.002 | 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".