“A right place for everybody”: Supporting aging in the right place for older people experiencing homelessness
Bibliographic record
Abstract
While policies and practices that promote aging in place have risen in prominence over the last two decades, marginalised older adults have largely been overlooked. 'Aging in the right place' is a concept that recognises the importance of adequate and appropriate age-related health and psychosocial supports in shelter/housing settings and their impact on the ability of older people to age optimally. To understand the unique shelter/housing challenges and solutions that affect aging in the right place for older people experiencing homelessness (OPEH), we conducted three World Café workshops in three Canadian cities-Montreal, Calgary, and Vancouver. In total, 89 service providers and OPEH engaged in the workshops, which involved guided, small-table discussions with the goal of stimulating creative ideas and fostering a productive atmosphere. Findings revealed two overarching themes 1) Discrepancies, between the need and availability of housing options and community supports for OPEH, such as affordable transportation, case management, access to healthcare, and system navigation; and 2) Desires, for more peer support, participatory planning, service-enriched housing, social programming, and policies that promote agency, independence, and choice for OPEH. These findings provide evidence to inform the development or modification of housing and supports for OPEH that promote aging 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".