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Record W2987385717 · doi:10.1093/geroni/igz038.1563

CUMULATIVE DIS/ADVANTAGE IN HEALTH AS AN INTERGENERATIONAL PROCESS

2019· article· en· W2987385717 on OpenAlexaff
Kim M. Shuey

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsLife course approachInequalityHealth equityProcess (computing)Mechanism (biology)PsychologyDemographic economicsDevelopmental psychologyEconomicsEconomic growthHealth careComputer science

Abstract

fetched live from OpenAlex

Abstract Empirical applications of cumulative dis/advantage primarily focus on accumulative processes within an individual life course as a mechanism generating growing intracohort inequality with age. However, adult health reflects the accumulation of exposures to advantages and disadvantages across the individual life course as well as the transmission of resources and practices across generations within one’s family of origin, which forms the foundation from which children are launched and inequality is reproduced. In analyses using two panel studies (PSID and Add Health), we integrate literature on the early-life origins of health with the concept of linked lives and the intergenerational transmission of resources to examine the relationship between parents’ health and the health of their children in adulthood. Results indicate an intergenerational persistence in health and demonstrate the importance of looking beyond the individual life course to better understand cumulative dis/advantage in health as a process operating across generations within families.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.003
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 head0.042
GPT teacher head0.443
Teacher spread0.400 · 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
Published2019
Admission routes1
Has abstractyes

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