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Record W4200220664 · doi:10.1093/geroni/igab046.2041

Residents' Wayfinding Challenges and Environmental Interventions in a Care Home

2021· article· en· W4200220664 on OpenAlexaffabout
Shelby Elkes, Habib Chaudhury

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychological interventionSession (web analytics)Baseline (sea)Focus groupPsychologyPhase (matter)Applied psychologyNursingComputer scienceMedicineBusinessMarketingPolitical science

Abstract

fetched live from OpenAlex

Abstract This study evaluated the role of the built environment on residents’ wayfinding behaviours at Louis Brier Home in Vancouver, British Columbia, Canada. The goal of this study was to explore baseline mobility challenges for the residents traveling between their bedrooms and social spaces. In response to this, low-cost environmental interventions were proposed and implemented to support safe and independent wayfinding for the residents. The project consisted of three phases. First phase involved a mixed methods approach using behavior mapping and spatial observations of the residents interacting with their physical environment, combined with one focus group with the staff members. In the second phase, researchers presented actionable environmental interventions for the care home administration to consider and implement. The final phase involved post-implementation behaviour mapping, spatial observations and a focus group session. The implemented environmental interventions influenced in improved resident wayfinding and orientation in the long-term care home.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.133
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.048
GPT teacher head0.321
Teacher spread0.273 · 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 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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