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Record W2595599540 · doi:10.1080/19485565.2016.1263150

Physical Functioning Trends among US Women and Men Age 45–64 by Education Level

2017· article· en· W2595599540 on OpenAlexaff
Anna Zajacova, Jennifer Karas Montez

Bibliographic record

VenueBiodemography and Social Biology · 2017
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsWestern University
FundersNational Institute on Aging
KeywordsDemographyGerontologyPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

Functional limitations and disability declined in the US during the 1980s and 1990s, but reports of early 21st century trends are mixed. Whether educational inequalities in functioning increased or decreased is also poorly understood. Given the importance of disability for productivity, independent living, and health care costs, these trends are critical to US social and health policies. We examine recent trends in functional limitations and disability among women and men aged 45-64. Using 2000-2015 National Health Interview Surveys data on over 155,000 respondents, semiparametric and logistic regression models visualize and test functioning trends by education. Among women and men with at least a college degree, there was no change in disability and mild increase in limitations over time. All other education levels experienced significant increases in functioning problems ranging from 18% higher odds of functional limitations in 2015 compared to 2000 among men with some college to about 80% increase in the odds of disability among women and men with less than high school education. The similar trends for both genders suggest common underlying causes, possibly including the worsening economic well-being of middle- and working-class families. The pervasive growth of functioning problems is a cause for concern that necessitates further scholarly investigation.

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)0.000
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 score0.378
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.040
GPT teacher head0.333
Teacher spread0.293 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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