1.3-O4Migrants and healthcare: a European comparative analysis under the economic-financial perspective
Bibliographic record
Abstract
Nowadays migration is one of the key issues in the international as well as in the European political and public debate. It represents a key challenge for modern societies and, together with the adaptation of welfare, has been extensively investigated in social sciences. One of the most compelling challenges consists in the adaptation of health systems to migration’s new needs. The European Union (EU) presents among its Member States highly differentiated situations in terms of healthcare provision models, contribution systems and integration policies adopted towards foreigners. Compared to other countries with a longer migratory tradition, the differences in access and use of health systems by intra-EU migrants and migrants from third countries are still considerable within EU Member States, and further diversified on the basis of migrants’ legal status. This becomes even clearer when considering the relationship between the Migration Integration Policy Index (MIPEX), carried out by the Migration Policy Group in 2015, and the data extracted from the 2014 Eurobarometer. Indeed, some country clusters are emerging and, in our work we will try to analyse the economic and financial peculiarities of different health systems in adapting to the new health questions of migrant citizens, bringing the empirical evidence of various case studies (Germany, Italy, UK and Spain in the EU vs. United States of America, Canada). Starting from the traditional types of healthcare systems, a more specific purpose will be to establish and measure the systematic relationship between the costs and performance of health systems, and migratory care demand and the migrants’ contribution to European systems.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".