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Record W4412722045 · doi:10.1080/19485565.2025.2539691

Status, despair, and epigenetic age acceleration: Chains of risk?

2025· article· en· W4412722045 on OpenAlexaff
Aniruddha Das

Bibliographic record

VenueBiodemography and Social Biology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpigeneticsAccelerationDemographyMedicineGeneticsBiologySociologyPhysicsGene

Abstract

fetched live from OpenAlex

Biological age acceleration predicts multiple "diseases of aging." Objective and subjective social statuses have both been prospectively linked to this outcome. An established chain-of-risk framework suggests that "effects" of each may be mediated by one's subsequent structural position. A separate deaths-of-despair literature identifies a person's sense of futility as another potential link. Such chains remain underexplored. The current study used data from three waves (2008-2016) of the Health and Retirement Study (HRS) to fill these gaps. The analysis was done through a counterfactual regression-with-residuals (RWR) approach. Asimulated decline in a person's objective but not subjective status predicted their age acceleration 8 years later. Contrary to chain-of-risk conceptions, intermediate social standing did not channel effects. Neither did despair. Findings were more consistent with a direct "material shocks" explanation for status-aging linkages than an indirect or psychosocial one. Implications for aging theory and for interventions are discussed.

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)0.005
metaresearch head score (Gemma)0.015
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 score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.345
Teacher spread0.318 · 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
Published2025
Admission routes1
Has abstractyes

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