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Record W4210311327 · doi:10.1093/geront/gnac023

Aging in the Right Place: A Conceptual Framework of Indicators for Older Persons Experiencing Homelessness

2022· article· en· W4210311327 on OpenAlexafffund
Sarah L. Canham, Rachel Weldrick, Tamara Sussman, Christine A. Walsh, Atiya Mahmood

Bibliographic record

VenueThe Gerontologist · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill UniversityUniversity of CalgarySimon Fraser University
FundersSocial Sciences and Humanities Research Council
KeywordsConceptualizationConceptual frameworkAging in placePsychologyUnit (ring theory)Conceptual modelThe Conceptual FrameworkPoliticsSociologyGerontologySocial psychologyPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Aging in place may not be a universally optimal goal nor accessible to all. Research has highlighted the significance of aging in the right place (AIRP) by recognizing that secure housing for older adults should support one's unique vulnerabilities and lifestyles. Despite the evolving conceptualization of AIRP for general populations of older adults, considerations of AIRP relevant for older people with previous or current experiences of homelessness are absent from the existing literature. Given this conceptual gap, we developed a framework of indicators relevant for older persons experiencing homelessness. We engaged community partners in the development of our framework and examined what had been described in prior research on aging in place and person-environment fit for older adults. The resulting conceptual framework is comprised of 6 subcategories of indicators: (a) built environment of the housing unit and surrounding neighborhood, (b) offsite and onsite health and social services and resources, (c) social integration, (d) stability and affordability of place, (e) emotional place attachment, and (f) broader political and economic contexts. This framework provides a practical and meaningful contribution to the literature which can be used to promote AIRP for individuals whose experiences are often not reflected in existing models.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.054
GPT teacher head0.405
Teacher spread0.351 · 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.

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

Citations40
Published2022
Admission routes2
Has abstractyes

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