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Record W619496525 · doi:10.1016/j.alcr.2015.05.004

Cumulative disadvantage, employment–marriage, and health inequalities among American and British mothers

2015· article· en· W619496525 on OpenAlexafffund
Peggy McDonough, Diana Worts, Cara Booker, Anne McMunn, Amanda Sacker

Bibliographic record

VenueAdvances in Life Course Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersInstitute of Gender and HealthEconomic and Social Research CouncilEuropean Research CouncilSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsDisadvantageInequalityLife course approachLongitudinal studyDemographic economicsNational Longitudinal SurveysNational Child Development StudySociologyLongitudinal dataPsychologyPolitical scienceDemographySocial psychologySocioeconomic statusEconomicsMedicineLaw

Abstract

fetched live from OpenAlex

This paper illuminates processes of cumulative disadvantage and the generation of health inequalities among mothers. It asks whether adverse circumstances early in the life course cumulate as health-harming biographical patterns across the prime working and family caregiving years. It also explores whether broader institutional contexts may moderate the cumulative effects of micro-level processes. An analysis of data from the British National Child Development Study and the US National Longitudinal Survey of Youth reveals several expected social inequalities in health. In addition, the study uncovers new evidence of cumulative disadvantage: Adversities in early life selected women into long-term employment and marriage biographies that then intensified existing health disparities in mid-life. The analysis also shows that this accumulation of disadvantage was more prominent in the US than in Britain.

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)0.004
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 score0.200
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.214
GPT teacher head0.568
Teacher spread0.354 · 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

Citations46
Published2015
Admission routes2
Has abstractyes

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