Late-Life Homelessness: A Definition to Spark Action and Change
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Comprehensive definitions of social issues and populations can set the stage for the development of responsive policies and practices. Yet despite the rise of late-life homelessness, the phenomenon remains narrowly understood and ill-defined. RESEARCH DESIGN AND METHODS: This article and the definition that ensued are based on the reconceptualization of interview data derived from a critical ethnography conducted in Montreal, Canada, with older homeless persons (N = 40) and service providers (N = 20). RESULTS: Our analysis suggests that definitions of late-life homelessness must include 4 intersecting components: (1) age, eligibility, and access to services; (2) disadvantage over the life course and across time; (3) social and spatial processes of exclusion that necessitate aging in "undesirable" places; and (4) unmet needs that result from policy inaction and nonresponse. DISCUSSION AND IMPLICATIONS: The new definition derived from these structural and relational components captures how the service gaps and complex needs identified in earlier works are shaped by delivery systems and practices whose effect is compounded over time. It provides an empirically grounded and conceptually solid foundation for the development of better responses to address homelessness in late life.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.074 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.016 |
| 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".