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Record W2900243512 · doi:10.1093/geroni/igy023.566

TRAJECTORY OF AGING COMMUNITIES: THE PATTERNS AND CHARACTERISTICS

2018· article· en· W2900243512 on OpenAlexaffabout
Y Lee, Kwang‐Yeon Choi

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTrajectoryPsychologyPhysics

Abstract

fetched live from OpenAlex

Objectives: While much research has been done on community-dwelling older adults, relatively less attention has been paid to the communities where older adults live. Of all the major cities in Canada, Calgary has the youngest population but the senior population is growing at a pace never seen before. This study examines the trajectory of growth in older adult population at the community level and investigates the characteristics of communities with high density of older adults. Methods: Using the census data from the 1991 to 2016 from Statistics Canada and spatial data from The City of Calgary, all analyses in this study was performed at the census tract level. Using a geographic information system (GIS), we map out how the communities have been changed over the last 25 years and identify the communities where older adults are likely to live. Results: Among the communities in Calgary, there are about 75% of communities with more than 7% of aged 65+ population. The growth in communities with older adults has spread out in the entire city and those communities are characterized as smaller household size, lower levels of incomes, fewer immigration population, lower levels of education, and fewer in the labor force. Discussions: The pattern of growth in communities with older adults and its characteristics have implications that older adults living in these communities might be more vulnerable. More emphasis should be put on community level to provide the purposes of longer lives for older adults.

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 score0.045
Threshold uncertainty score0.655

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 head0.065
GPT teacher head0.360
Teacher spread0.294 · 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
Published2018
Admission routes2
Has abstractyes

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