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Record W3187970927 · doi:10.1093/geront/gnab115

Contextualizing Innovative Housing Models and Services Within the Age-Friendly Communities Framework

2021· article· en· W3187970927 on OpenAlexaff
Atiya Mahmood, Kishore Seetharaman, Hailey-Thomas Jenkins, Habib Chaudhury

Bibliographic record

VenueThe Gerontologist · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAutonomySustainabilityDiversity (politics)BusinessInclusion (mineral)Aging in placePublic relationsEconomic growthPsychologyGerontologySociologyPolitical scienceMedicineEconomicsSocial psychology

Abstract

fetched live from OpenAlex

This article compares and contrasts the characteristics of 3 models of housing and services for older adults, cohousing, Naturally Occurring Retirement Community Supportive Services Program, and villages, and links them to the domains of the age-friendly communities (AFCs) framework, specifically (a) services, supports, and information; (b) respect, inclusion, and diversity; (c) social and civic participation; and (d) affordability. We discuss key barriers and challenges of these models with respect to the AFC domains, as well as implementation and sustainability. Consideration of these models in age-friendly housing policy and practice could help expand and diversify the choices in the housing and services continuum. This aligns with AFC's emphasis on the need for housing and services responsive to older adults' diverse health and social needs, provides options that balance autonomy, choice, and support, and emphasizes older adults' participation and involvement in tailoring these options.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0080.016
Scholarly communication0.0060.006
Open science0.0020.013
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.337
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2021
Admission routes1
Has abstractyes

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