What role does socio-economic position play in the link between functional limitations and self-rated health: France vs. USA?
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".