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Record W2918678616 · doi:10.1177/0898264319831513

Social Integration, Self-Rated Health . . . and Genes?

2019· article· en· W2918678616 on OpenAlexaff
Aniruddha Das

Bibliographic record

VenueJournal of Aging and Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsSelf-rated healthPsychologyMedicineGerontology

Abstract

fetched live from OpenAlex

Objective: This study examined genetic roots of later life social integration, and their confounding of this social factor’s health linkages. Method: Data were from the 2010 wave of the Health and Retirement Study. Two dimensions of integration were examined: with one’s “stakeholder” network of family and friends and with the community. Genetic measures included polygenic scores for extraversion and educational attainment. Results: Ties to one’s stakeholder network had no genetic associations. The extraversion polygenic score was linked to community integration among Blacks as well as Whites. Among the latter, the same was true of one’s genetic propensity for educational attainment. Although this score also influenced self-rated health, neither confounded associations of social integration with this indicator. Discussion: Later life social integration seems influenced by genetically rooted propensities for both sociability and human capital accumulation. Health linkages of integration, however, may not reflect mutual dependencies on the same genetic substrates.

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)0.001
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.410
Teacher spread0.360 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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