Health Inequalities and the Welfare State in European Families
Bibliographic record
Abstract
Using EU-Silc data from 2005, our aim in this article is to estimate how self-assessed health and the gradient between education and health vary among individuals in different European countries, considering their contextual socioeconomic vulnerability. In order to do this, we use a hierarchical model with individuals nested in households at the second level, and in various European countries at the third level. Our main research interest is on the modelling variables associated with better health conditions and their improvement or worsening according not only to micro/ individual and macro/national levels but also to the household: a level on which social protection (of whatever nature) exerts its influence. Diferent household contexts receive different amounts of resources, by transfers, social care and health services, which could directly affect health and also modify the gradient between education and health. Moreover, these relations are likely to change among European countries, on the basis of various welfare assets, as the identification of beneficiaries' categories and the weight of category-based measures on the overall welfare expenditure varies among countries and among welfare models.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".