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Record W7095510362

Decision Editor: Merril Silverstein, PhD Race/Ethnic Differentials in the Health Consequences of Caring for Grandchildren for Grandparents

2013· article· en· W7095510362 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsnot available
Fundersnot available
KeywordsGrandparentHealth and Retirement StudyGrandchildReceiptQuarter (Canadian coin)Longitudinal studyLongitudinal data
DOInot available

Abstract

fetched live from OpenAlex

Objectives. The phenomenon of grandparents caring for grandchildren is disproportionately observed among different racial/ethnic groups in the United States. This study examines the influence of childcare provision on older adults ’ health trajectories in the United States with a particular focus on racial/ethnic differentials. Method. Analyzing nationally representative, longitudinal data on grandparents over the age of 50 from the Health and Retirement Study (1998–2010), we conduct growth curve analysis to examine the effect of living arrangements and caregiv-ing intensity on older adults ’ health trajectories, measured by changing Frailty Index (FI) in race/ethnic subsamples. We use propensity score weighting to address the issue of potential nonrandom selection of grandparents into grandchild care. Results. We find that some amount of caring for grandchildren is associated with a reduction of frailty for older adults, whereas coresidence with grandchildren results in health deterioration. For non-Hispanic black grandparents, living in a skipped generation household appears to be particularly detrimental to health. We also find that Hispanic grandparents fare better than non-Hispanic black grandparents despite a similar level of caregiving and rate of coresidence. Finally, financial and social resources assist in buffering some of the negative effects of coresidence on health (though this effect also differs by race/ethnicity).

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.996

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 studies0.0000.000
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 head0.071
GPT teacher head0.359
Teacher spread0.289 · 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 designOther design
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
Published2013
Admission routes1
Has abstractyes

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