EXPLORING PROMISING PRACTICE MODELS FOR HOUSING OLDER PERSONS EXPERIENCINGHOMELESSNESS
Bibliographic record
Abstract
Abstract The numbers of older persons experiencing homelessness (OPEH) is on the rise globally Yet housing and shelter options that support the varied and complex needs of this population are scarce. In order to understand effective solutions for housing OPEH, it is critical to explore promising practices that support aging in the right place for OPEH. In an effort to inform this critical gap, 100 OPEH and service providers were purposefully selected and invited to attend one of three World Café workshops held in three major urban cities in Canada: Vancouver, Calgary, and Montréal. Participants engaged in facilitated discussions aimed at supporting knowledge exchange and generating dialogue about gaps, opportunities and promising local housing options. Thematic analyses of audiotaped deliberations revealed three themes: 1) The limited nature of current housing options and programs in each locality; 2) The importance of supporting integrative housing models that increase access to formal health and social support staff, transportation, and income supports; and 3) The significance of supporting sustainability, by conducting regular program evaluations, increasing public awareness of homelessness issues, and involving multi-sector stakeholders. Findings highlight how meeting the unique health and psychosocial needs of OPEH requires a nuanced understanding of the development, design, and sustainability of effective housing options. World Café dialogues revealed that identifying and sustaining existing promising practice models provides an avenue to supporting aging in the right place for OPEH.
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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 | 0.044 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".