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Record W4400422426 · doi:10.1080/26892618.2024.2368540

Effects of Sensory Environment and Playfulness on Cognitive Health Among Older Adults in Singapore Public Housing: Findings from Path Analysis

2024· article· en· W4400422426 on OpenAlexaff
Zdravko Trivic, Yi Jie Chin, Ted Kheng Siang Ng, Alan Prem Kumar, Clarissa E. Hui Lee, Paul D. Slowey, Patrick Dillon, Tam Perry, Daniel R Y Gan

Bibliographic record

VenueJournal of Aging and Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsPublic Health OntarioUniversity of TorontoSimon Fraser University
FundersAcadémie Nationale de MédecineNational Research FoundationMinistry of Health – Kingdom of Saudi Arabia
KeywordsPath analysis (statistics)CognitionPath (computing)PsychologySensory systemGerontologyMedicineCognitive psychologyComputer scienceMathematicsStatisticsPsychiatry

Abstract

fetched live from OpenAlex

This study explores the association of “Sensory Environment” and “Playfulness” with Depression and Cognition (SEPDC) among older adults living in high-density public housing neighborhoods in Singapore. The research conducted cross-sectional surveys with 400 adults aged 55 and above living in 20 such neighborhoods. Path analyses suggest that sensory environment (multi-sensory richness of a place and its capacity to engage) and playfulness (personality trait that enables rendering situations as playful) are associated with reduced depressive symptoms, memory problems, and loneliness in older adults, mediated by the increased “neighborhood cohesion” and sense of “at-homeness.” Hence, sensory environment and playfulness are critical for healthful aging in place and should be considered carefully for better design and planning of aging-friendly housing neighborhoods.

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 score0.023
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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