“No Home, No Place”: Addressing the Complexity of Homelessness in Old Age Through Community Dialogue
Bibliographic record
Abstract
The aging-in-place agenda supports the right of seniors to live in their preferred environment, as the place where they can retain a sense of independence and control in old age. This right is compromised for vulnerable seniors who are homeless or at risk of becoming homeless. Causes of homelessness in old age are complex, and pathways into and out of homelessness are multifaceted, including financial insecurity, relationship breakdown, and addiction, compounded by barriers to accessing services, shrinking social support networks, and complex health challenges. Addressing the multidimensional nature of homelessness in old age requires holistic solutions that bring together the knowledge and expertise of multiple stakeholders, not least seniors themselves. With this aim, this paper reports on findings from multistakeholder community dialogue sessions conducted across Metro Vancouver with seniors’ organizations, service providers, and local government to prioritize the challenges of senior homelessness in Metro Vancouver and propose strategies and solutions for addressing the issue. The paper highlights some of the ways in which services and housing supports can be designed to support older adults who are homeless or at risk of becoming homeless.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| 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".