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Record W4224081161 · doi:10.1177/00914150221092129

Functional Disability Among Middle-Aged and Older Adults in China: The Intersecting Roles of Ethnicity, Social Class, and Urban/Rural Residency

2022· article· en· W4224081161 on OpenAlexaff
Shen Lin

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEthnic groupChinaOddsMultinomial logistic regressionGerontologyDemographyLogistic regressionSocioeconomic statusActivities of daily livingMedicinePovertyRural areaOdds ratioSocial classGeographyEnvironmental healthSociologyPolitical sciencePopulation

Abstract

fetched live from OpenAlex

This study explores how ethnicity, family income, and education level differentiate patterns of functional limitations among urban and rural Chinese (aged 45 ≥ years). Based on the 2018 China Family Panel Studies (CFPS) (n = 16,589), this nationwide study employed binary/multinomial logistic regression analyses, stratified by urban/rural residency, to estimate the likelihood of instrumental activities of daily living (IADLs) disability (0/1-2/≥3 limitations) by social determinants of health (SDoH). The estimated overall prevalence of IADLs disability was 14.3%. The multivariable analyses did not find significant ethnic disparity in IADLs disability in urban China, while in rural China, ethnic minorities were 44% more likely to have IADLs disability than Han Chinese. Among rural residents, Mongolians, Tibetans, and Yi minority more than tripled the odds of having ≥3 limitations than Han Chinese; and the intersections of ethnicity and social class were associated with functional limitations. Long-term care and anti-poverty programs should target minority aging populations in rural China.

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: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.771

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 head0.030
GPT teacher head0.307
Teacher spread0.277 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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