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Record W2306645133 · doi:10.14301/llcs.v7i1.352

Heterogeneity in chronic disease outcomes among women and men in midlife: examining the role of stability and change in childhood economic hardship

2016· article· en· W2306645133 on OpenAlexaff
Cole Etherington, Andrea E. Willson, Kim M. Shuey

Bibliographic record

VenueLongitudinal and Life Course Studies · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyGerontologyChronic diseaseLife course approachDevelopmental psychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

In this study, we advance existing research on the long-term effects of childhood disadvantage on health in adulthood by examining how the timing and duration of childhood economic hardship differentiates between those at low and high risk of chronic disease onset in midlife for women and men, across four different health outcomes. The study uses prospective data on childhood and adulthood from the US Panel Study of Income Dynamics. Discrete time hazard models are estimated using logistic regression to determine how experiences of childhood economic hardship affect the risk of disease onset in midlife differently for men and women. Results indicate that, in general, childhood economic context results in an increased risk of multiple diseases for women but not for men. Specifically, women who experienced long-term economic hardship in childhood, or began life in poverty but moved out of poverty in childhood, were more likely to experience the onset of diabetes, arthritis, and cardiovascular diseases in midlife, net of other factors, such as adult resources. The impact of childhood economic hardship on disease onset also varied by age for women but not for men, and this relationship was also dependent on the health outcome examined. This study draws attention to the importance of conceptualizing and measuring childhood disadvantage as dynamic, and reveals that the process of cumulative disadvantage may be different for women and men.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.102
GPT teacher head0.411
Teacher spread0.309 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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