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Record W4386257271 · doi:10.1177/00914150231196092

Black–White Racial Disparities in Disabilities Among Older Americans Between 2008 and 2017: Improvements in Cognitive Disabilities but no Progress in Activities of Daily Living or Functional Limitations

2023· article· en· W4386257271 on OpenAlexaff
Bolade Ajarat Shipeolu, Katherine Marie Ahlin, Esme Fuller‐Thomson

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
FundersUniwersytet Medyczny im. Karola Marcinkowskiego w Poznaniu
KeywordsHealth equityGerontologyWhite (mutation)Race (biology)CognitionRacial differencesActivities of daily livingMedicinePsychologyDemographyEthnic groupPublic healthPhysical therapyPolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

The objectives of this study were to examine the prevalence of race-based disparities in cognitive problems, functional limitations (FLs), and activity of daily living (ADL) limitations between US Black and White older adults in 2008 and 2017, to explore how age, sex, income, and education attenuate these racial disparities, and to determine if Black–White health disparities are narrowing. Secondary analysis of the nationally representative American Community Surveys including 423,066 respondents aged ≥65 (388,602 White, 34,464 Black) in 2008 and 536,984 (488,483 White, 48,501 Black) in 2017. Findings indicate that Black–White racial disparities were apparent for all three outcomes in 2008 and 2017. Approximately half of the racial disparities was attenuated when adjustments were made for education and income. Racial disparities in cognition declined between 2008 and 2017 ( p < .001) but persisted unabated in FLs and ADL limitations. Further exploration on the mechanisms of racial disparities is warranted.

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)0.001
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 score0.035
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.080
GPT teacher head0.366
Teacher spread0.286 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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