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Record W4226424871 · doi:10.1142/s0116110522500056

Educational Gradients in Disability among Asia’s Future Elderly: Projections for the Republic of Korea and Singapore

2022· article· en· W4226424871 on OpenAlexaff
CYNTHIA CHEN, Jue Tao Lim, Ngee Choon Chia, Dae Jung Kim, HAEMI PARK, Lijia Wang, Bryan Tysinger, Michelle Zhao, Alex R. Cook, Ming Zhe Chong, Jian‐Min Yuan, Stefan Ma, Kelvin Bryan Tan, Tze Pin Ng, Woon‐Puay Koh, Joanne Yoong, Jay Bhattacharya, Karen Eggleston

Bibliographic record

VenueAsian Development Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsActuaUniversity of Waterloo
Fundersnot available
KeywordsEducational attainmentGerontologyMicrosimulationEast AsiaMedicineDemographyChinaGeographyEconomic growthSociologyEconomics

Abstract

fetched live from OpenAlex

Asia is home to the most rapidly aging populations in the world. This study focuses on two countries in Asia that are advanced in terms of their demographic transition: the Republic of Korea and Singapore. We developed a demographic and economic state-transition microsimulation model based on the Korean Longitudinal Study of Aging and the Singapore Chinese Health Study. The model was employed to compare projections of functional status and disability among future cohorts of older adults, including disparities in disability prevalence by educational attainment. The model also projects increasing disparities in the prevalence of activities-of-daily-living disability and other chronic diseases between those with low and high educational attainment. Despite overall increases in educational attainment, all elderly, including those with a college degree, experience an increased burden of functional disability and chronic diseases because of survival to older ages. These increases have significant economic and social implications, including increased medical and long-term care expenditures, and an increased caregiver burden.

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)0.002
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: none
Teacher disagreement score0.761
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.034
GPT teacher head0.350
Teacher spread0.316 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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