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Record W4200307974 · doi:10.1093/geroni/igab046.1130

Evaluating Spaces for Older Adults Experiencing Homelessness: Findings From an Environmental Audit

2021· article· en· W4200307974 on OpenAlexaffabout
Hannah Brais, Émilie Cormier, Diandra Serrano, Atiya Mahmood, Tamara Sussman, Valérie Bourgeois-Guérin

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSimon Fraser UniversityMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsRecreationAging in placeAuditChecklistBuilt environmentInclusion (mineral)Social exclusionIndependence (probability theory)PsychologySociologyPublic relationsGerontologyPolitical scienceBusinessSocial psychologyMedicineEngineering

Abstract

fetched live from OpenAlex

Abstract Homeless populations require spaces and services that take into account their life trajectories. The Aging in the Right Place - Environmental Checklist (AIRP-ENV) is an environmental audit tool developed by our team to evaluate the accessibility and overall design features of housing targeted for aging individuals experiencing homelessness. Researchers in Vancouver, Calgary and Montreal employed this tool in 2021 to evaluate environmental features in selected promising practices to identify built environment factors that promote aging in the right place. Preliminary findings reveal the following themes across sites: access to communal and recreational spaces encourage social inclusion and meaningful recreation opportunities; barrier-free built environment features foster independence and safety; and access to services and amenities encourage community mobility. Findings demonstrate a need to employ a broader evaluative lens that incorporates psycho-social factors to gain a nuanced understanding of aging in the right place for older adults who have experienced homelessness.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.696

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.438
Teacher spread0.372 · 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 routes2
Has abstractyes

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