Old and Homeless: A Review and Survey of Older Adults Who Use Shelters in an Urban Setting
Bibliographic record
Abstract
OBJECTIVES: Research on the mental health and service needs of homeless seniors has been scant. This paper reviews the available literature and presents findings of a Toronto survey in an effort to describe the demographics of homeless seniors, their level of impairment, and their mental and physical health needs. METHODS: We searched the Medline, AgeLine, and PsycINFO databases, using the following key words: elderly homeless, elderly hostel users, and urban geriatrics. To better describe the service needs of the elderly homeless, we obtained demographic data from the Community and Neighbourhood Services Department and distributed a survey questionnaire to 11 Toronto hostel directors. The questionnaire elicited data relating to reasons for shelter use, problem behaviours, and mental health needs of those over age 65 years. RESULTS: Although seniors represent a small percentage of the homeless population, their numbers are growing. The available literature suggests a high prevalence of psychiatric disorders and cognitive impairment in this population, with a greater proportion of older women than men having severe mental illness. Further, our survey suggests that the service needs of elderly hostel users in Toronto differ from those of their younger counterparts. CONCLUSION: The homeless elderly are the most vulnerable of this impoverished population. Although more research is needed to define their mental and physical health needs and ways of meeting them, their characteristics appear to be unique. Geriatric psychiatrists could play a significant role in evaluating and treating this population more comprehensively.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".