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Record W4200454266 · doi:10.1093/geroni/igab046.1128

Aging in the Right Place: A Conceptual Framework for Housing Insecure Older People

2021· article· en· W4200454266 on OpenAlexaff
Rachel Weldrick, Sarah L. Canham, Atiya Mahmood, Tamara Sussman, Christine A. Walsh

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsMcGill UniversityUniversity of CalgarySimon Fraser University
Fundersnot available
KeywordsAging in placeConceptual frameworkOlder peopleConceptual modelOrder (exchange)Public relationsSociologyPsychologyPolitical scienceBusinessGerontologyComputer scienceMedicineSocial scienceFinance

Abstract

fetched live from OpenAlex

Abstract Emerging research has highlighted the significance of aging in the right place (AIRP) by recognizing that secure and optimal housing should support an individual’s unique vulnerabilities and lifestyles. Existing literature, however, has yet to consider what it means for older people experiencing homelessness and/or housing insecurity to age-in-the-right-place. In order to address this knowledge gap, a review of person-environment fit models for older people and other relevant literature was conducted to determine critical identifiers of AIRP for housing insecure older people. Findings from this literature review were then refined in collaboration with interdisciplinary scholars and community partners to establish a conceptual framework. This paper presents the resulting conceptual framework and outlines the key indicators of AIRP relevant to housing insecure older people. The proposed framework provides a practical and meaningful contribution to the literature which can be used to promote housing security among individuals often excluded from existing aging-in-place 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)0.001
metaresearch head score (Gemma)0.001
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 score0.223
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.340
Teacher spread0.310 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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