AGING IN THE RIGHT PLACE: TEMPORARY SUPPORTIVE HOUSING FOR OLDER MALE VETERANS IN CALGARY, CANADA
Bibliographic record
Abstract
Abstract In 2019 an estimated 1.6% of emergency shelter users in Canada were veterans (1,905 individuals), which is consistent with the proportion of veterans in the general population (1.7%). However, veterans tend to be older, male, and cite an illness or medical condition as a contributing factor to their homelessness, than their non-veteran counterparts. As part of the Aging in the Right Place (AIRP) study, we sought to understand the housing and support needs of older male military veterans (age 50+) living in a congregate temporary supportive housing in Calgary, Alberta. This exploratory, multi-methods study used: (1) de-identified document review, (2) environmental audit, (3) in-depth qualitative key informant interviews with service providers and (4) in-depth qualitative and Photovoice interviews with older shelter residents to understand the shelter needs of older homeless veterans. The data was collected between February and June 2023. Interviews were transcribed, managed with NVivo 1.6.1, and team-based flexible coding was employed on the key informant (n=5) and shelter resident (n=5) interviews to determine older male veterans’ shelter needs. In this presentation we share the findings of the study and offer recommendations for shelter and services designed to assist older housing insecure veterans to age in the right place.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| 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".