Health and access to care among vulnerable populations in Europe: Findings from the 2015 Doctors of the World International Network Observatory
Bibliographic record
Abstract
Background Since 2006, the Doctors of the World (MdM) International Network Observatory has conducted multicenter surveys in our free clinics across Europe & Canada among people facing multiple vulnerabilities, (EU nationals & migrants, 3rd country citizens). These surveys describe their health states and social determinants of health, including obstacles to access to care, with the aim of informing health policy makers and obtain positive changes. Materials and Methods A cross-sectional analysis of routine data collected from 34,300 patients in 94,453 social and medical consultations at MdM & partners health centers in 13 countries in 2015 (Belgium, Canada, France, Germany Greece, Luxemburg, the Netherlands, Norway, Spain, Sweden, Switzerland, Turkey and UK). An analysis of the legislative context regarding access to care was made. Results The vast majority of patients consulted our clinics for medical care while around 20% of patients were seeking social assistance. The population consisted mainly of migrants (EU migrants included), however a good part of patients seen in Greece & Germany were nationals. Around 80% of patients seen did not have any health coverage. Nearly 100% of patients were living below the poverty level of the country. The obstacles to access to care were primarily due to restrictive legislations; financial, administrative or lack of knowledge about rights & local health system, or language barrier. The immigrants interviewed had been living in their host country long term before consulting. Very few patients with a chronic disease knew about it before migrating or cited health as a reason for migration. Discussion The populations seen by MdM live in particularly disadvantaged conditions. They need more (and certainly not less) protection and to be given easier access to care. There is no tangible argument or public health justification for using health care and access-to-care policies as means of regulating migration flows. Key messages: Major inequalities in health states & social determinants of health, including obstacles to access to care, are found in the vulnerable populations using MdM & partners clinics in Europe & Canada MdM urges Member States and EU institutions to ensure universal public health systems built on solidarity, equality and equity, open to everyone living in an EU Member State
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".