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Record W2092111020 · doi:10.1093/eurpub/ckr056

What role does socio-economic position play in the link between functional limitations and self-rated health: France vs. USA?

2011· article· en· W2092111020 on OpenAlexaff
Cyrille Delpierre, Geetanjali D. Datta, Michelle Kelly‐Irving, V. Lauwers‐Cancès, Lisa Berkman, Thierry Lang

Bibliographic record

VenueEuropean Journal of Public Health · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de Montréal
FundersCenters for Disease Control and Prevention
KeywordsPosition (finance)Link (geometry)PsychologyEnvironmental healthGerontologyMedicineEconomicsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Our objective was to analyse the influence of education on the link between functional limitation (FL) and self-rated health (SRH) in two countries, France and the USA. METHODS: The data of the North American NHANES study (n = 9254) and the French National Health Survey (n = 25 559) were used. FL was measured by the ADL and IADL scales. We constructed a logistic regression model with SRH as the outcome and included variables for education, FL and the interaction between education and FL. All results were adjusted for age. RESULTS: Poor SRH was more frequently reported in France than in the USA (24.1% vs. 18.4% for men, 29.0% vs. 19.7% for women). The most highly educated persons in the USA had similar FL (25.4% for men, 32.9% for women) to the least educated French persons (22.8% for men, 31.8% for women). In the USA, FL was associated more strongly with poor SRH in the most educated men than in the least educated. In France, the same interaction was observed although the link was weaker than in the USA. FL was more strongly associated with poor SRH in the most educated women than in the least educated in both countries. CONCLUSION: Functional limitation had a greater impact on the most highly educated persons in both France and the USA. Using SRH as a measure of health for evaluating social inequalities could lead to underestimation of the true magnitude of functional health inequalities existing within and between countries.

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)0.018
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: none
Teacher disagreement score0.527
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.121
GPT teacher head0.324
Teacher spread0.203 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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