MétaCan
Menu
Back to cohort
Record W4411731701 · doi:10.1093/geronb/gbaf119

Healthy working life expectancy across birth cohorts in the United States

2025· article· en· W4411731701 on OpenAlexaff
Félix Blain, Michaël Boissonneault

Bibliographic record

VenueThe Journals of Gerontology Series B · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversité de Montréal
FundersNational Institute on AgingUniversity of Michigan
KeywordsLife expectancyExpectancy theoryPsychologyDemographyDevelopmental psychologyGerontologyDemographic economicsSocial psychologyMedicineSociologyEconomicsPopulation

Abstract

fetched live from OpenAlex

OBJECTIVES: This study investigates trends in healthy working life expectancy (HWLE) in the United States amid changing retirement conditions and recent declines in health among working-age individuals. HWLE, defined as the average number of years expected to be spent healthy and working between ages 51 and 80, is examined by gender and educational level across three birth cohorts. METHODS: Using longitudinal data from the Health and Retirement Study (HRS), HWLE estimates were calculated for individuals aged 51 and older. Using continuous-time multistate modelling, trends were analyzed across three birth cohorts (1936-1941, 1942-1947, 1948-1953), focusing on differences by gender and educational attainment to assess disparities in HWLE over time. RESULTS: The findings indicate that HWLE remained stable for most groups but declined among individuals with lower educational attainment. Thus, as early retirement becomes increasingly costly and risky, workers appear unable to extend their working lives and are facing growing inequalities. DISCUSSION: These findings highlight the need for targeted policies to promote healthier work environments and expanded job opportunities for older adults. Addressing disparities in HWLE, particularly for those with less education, is critical to improving outcomes for future cohorts as they approach retirement age.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.268
GPT teacher head0.481
Teacher spread0.213 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series BSame topicRetirement, Disability, and EmploymentFrench-language works237,207