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Record W4231365217 · doi:10.1093/geront/gnw162.2991

PREDICTORS OF NEUROCOGNITIVE PERFORMANCE AMONG AFRICAN AMERICANS ENROLLED IN THE HANDLS STUDY

2016· article· en· W4231365217 on OpenAlexaff
Margaret J. Penning, Zheng Wu, Jianjun Liu, Denise Burnette, Xin Huang, Nan Jiang

Bibliographic record

VenueThe Gerontologist · 2016
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNeurocognitivePsychologyInternal medicineMedicineClinical psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

and marital unions).Based on dyadic interviews of nine older adult couples (N=18) (ages between 45-80 years), we find that social norms, familial networks, religion, economic status, marital histories and the experience of widowhood/bereavement influence re-partnering choices.In particular, caste and class based differences were important mediators in bridging the normative script and the actual reality.Again, gendered perceptions and expectations around aging, domesticity and health motivated men and women differently to enter into such unions.Overall, both older men and women navigated their post-reproductive lives to re-adapt themselves to the contrary pulls of social sanctioning with individualistic dimensions of autonomy, companionship and sexuality.The study contributes to the cumulative knowledge building of gerontology by examining continuity, transition and heterogeneity of intimate relationships among older Indians.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.354
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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