Shelter/housing options, supports and interventions for older people experiencing homelessness
Bibliographic record
Abstract
Abstract While experiences of later-life homelessness are known to vary, classification of shelter, housing and service models that meet the diverse needs of older people with experiences of homelessness (OPEH) are limited. To address this gap, a scoping review was conducted of shelter/housing options, supports and interventions for OPEH. Fourteen databases were searched for English-language peer-reviewed and/or empirical literature published between 1999 and 2019, resulting in the inclusion of 22 sources. Through a collaborative, iterative process of reading, discussing and coding, data extracted from the studies were organised into six models: (1) long-term care, (2) permanent supportive housing (PSH), including PSH delivered through Housing First, (3) supported housing, (4) transitional housing, (5) emergency shelter settings with health and social supports, and (6) case management and outreach. Programme descriptions and OPEH outcomes are described and contribute to our understanding that multiple shelter/housing options are needed to support diverse OPEH. The categorised models are considered alongside existing ‘ageing in place’ research, which largely focuses on older adults who are housed. Through extending discussions of ageing in the ‘right’ place to diverse OPEH, additional considerations are offered. Future research should explore distinct sub-populations of OPEH and how individual-level supports for ageing in place must attend to mezzo- and macro-level systems and policies.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".