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Record W2726244870 · doi:10.1093/geronb/gbx096

Trajectories of Work Disability and Economic Insecurity Approaching Retirement

2017· article· en· W2726244870 on OpenAlexafffund
Kim M. Shuey, Andrea E. Willson

Bibliographic record

VenueThe Journals of Gerontology Series B · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsWestern University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institutes of HealthNational Science Foundation
KeywordsWork (physics)PsychologyEconomicsDemographic economicsLabour economicsEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: In this article, we examine the connection between trajectories of work disability and economic precarity in late midlife. We conceptualize work disability as a possible mechanism linking early and later life economic disadvantage. METHODS: We model trajectories of work disability characterized by timing and stability for a cohort of Baby Boomers (22-32 in 1981) using 32 years of data from the Panel Study of Income Dynamics and latent class analysis. Measures of childhood disadvantage are included as predictors of work disability trajectories, which are subsequently included in logistic regression models predicting four economic outcomes (poverty, asset poverty, home ownership, and pension ownership) at ages 54-64. RESULTS: Childhood disadvantage selected individuals into five distinct classes of work disability that differed in timing and stability. All of the disability trajectories were associated with an increased risk of economic insecurity in late midlife compared to the never work disabled. DISCUSSION: This study contributes to the aging literature through its incorporation of the early life origins of pathways of disability and their links to economic outcomes approaching retirement. Findings suggest work disability is anchored in early life disadvantage and is associated with economic insecurity later in life.

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 score0.020
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
Bibliometrics0.0000.000
Science and technology studies0.0010.006
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 head0.301
GPT teacher head0.443
Teacher spread0.141 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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