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Record W4402118345 · doi:10.1093/geront/gnae123

Late-Life Homelessness: A Definition to Spark Action and Change

2024· article· en· W4402118345 on OpenAlexafffundabout
Amanda Grenier, Tamara Sussman

Bibliographic record

VenueThe Gerontologist · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill UniversityBaycrest HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAction (physics)SPARK (programming language)PsychologySociologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.298
GPT teacher head0.453
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes3
Has abstractyes

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