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Record W4200055368 · doi:10.1093/geroni/igab046.2039

Environmental Assessment of Housing for Older Adults Facing Housing Insecurity

2021· article· en· W4200055368 on OpenAlexaffabout
Shreemouna Gurung, Shelby Elkes, Atiya Mahmood, Muhammad Fazal Hussain Qureshi, Habib Chaudhury, Sarah L. Canham, Rachel Weldrick

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAging in placeGeneral partnershipAuditBuilt environmentBusinessFunction (biology)Environmental healthEnvironmental planningGeographyGerontologyEngineeringMedicineCivil engineeringFinance

Abstract

fetched live from OpenAlex

Abstract The Aging in the Right Place Environmental Audit (AIRP-ENV) and Secondary Observation (AIRP-ENV-SO) tools were developed to conduct observation-based audit of the built environment in shelters, transitional housing, independent housing with offsite/onsite supports, and permanent supportive housing with onsite medical and/or specialized services for older adults experiencing (or at risk of) homelessness. The 241 item AIRP-ENV tool is used to audit the presence/absence of exterior and interior built environmental features that support housing stability. The seven open-ended questions in the AIRP-ENV-SO tool is used to collect contextual data on function, safety and land-use of surrounding neighborhood. Data were collected at four sites of a transitional housing program in Vancouver, Canada as part of a multi-year, multi-city partnership project on aging and homelessness. Preliminary results demonstrate that built environment and urban design features (e.g., access, privacy, flexible and supportive spaces) contribute towards tenants’ residential resiliency and aging in place.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.336
Teacher spread0.314 · 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 designObservational
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

Citations0
Published2021
Admission routes2
Has abstractyes

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