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Record W3124788880 · doi:10.1177/0733464820978000

Identifying Communities of Concern for Older Adults Using Spatial Analysis: Focusing on Accessibility to Health, Social, and Daily Services

2021· article· en· W3124788880 on OpenAlexafffund
Kwangyul Choi, Yeonjung Lee, Zoe Basrak

Bibliographic record

VenueJournal of Applied Gerontology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsAging in placePerspective (graphical)Geographic information systemGerontologyGeographyEnvironmental healthMedicineComputer science

Abstract

fetched live from OpenAlex

Ensuring accessibility to necessary services is critical for older adults. However, there often exist spatial disparities in the levels of accessibility to services. Because the application of Geographic Information System (GIS) has gained attention in the gerontology field, we used spatial analysis to identify communities of concern for older adults from the perspective of accessibility. We defined the communities of concern based on the proportion of older adults and the level of accessibility to health, social, and daily services via two specific modes of transportation-walking and public transit. Our findings show that newly developed communities tend to have less accessibility to necessary services, and aging communities are randomly distributed across the city. Our results call for interdisciplinary collaboration, between urban planning and gerontology professionals, to better understand the spatial pattern of aging communities and its implication for properly addressing the mobility needs of older adults in the communities.

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 score0.278
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.102
GPT teacher head0.405
Teacher spread0.303 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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