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Record W4210612649 · doi:10.1177/09567976211036061

Early-Life Socioeconomic Circumstances and Physical Activity in Older Age: Women Pay the Price

2022· article· en· W4210612649 on OpenAlexafffund
Aïna Chalabaëv, Stefan Sieber, David Sander, Stéphane Cullati, Silvio Maltagliati, Philippe Sarrazin, Matthieu P. Boisgontier, Boris Cheval

Bibliographic record

VenuePsychological Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsBruyèreUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute on AgingSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungBanting Research Foundation
KeywordsSocioeconomic statusPsychologyContext (archaeology)ConfoundingGerontologyLife course approachDisadvantageScale (ratio)DemographyAgeingCohortPopulationDevelopmental psychologyMedicineGeographySociology

Abstract

fetched live from OpenAlex

Health in older age is shaped by early-life socioeconomic circumstances (SECs) and sex. However, whether and why these factors interact is unclear. We examined a cultural explanation of this interaction by distinguishing cultural and material aspects of SECs in the context of physical activity-a major determinant of health. We used data from 56,331 adults between 50 and 96 years old from the Survey of Health, Ageing and Retirement in Europe (SHARE), a 13-year, large-scale, population-based cohort. Confounder-adjusted logistic linear mixed-effects models showed an association between the cultural aspects of early-life SEC disadvantage and physical activity among women, but it was not consistently observed in men. Furthermore, these associations were compensated for only partially by adult-life socioeconomic trajectories. The material aspects of early-life SECs were not associated with adult-life physical activity. These findings highlight the need to distinguish different aspects of SECs because they may relate to health behaviors in diverse ways.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.044
GPT teacher head0.394
Teacher spread0.350 · 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.

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

Citations56
Published2022
Admission routes2
Has abstractyes

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