What health care for undocumented migrants in Belgium?
Bibliographic record
Abstract
FOREWORD 1 -- KEY MESSAGES 2 -- SYNTHESIS 3 -- 1. WHO ARE THE UNDOCUMENTED MIGRANTS? 6 -- 2. WHAT IS URGENT MEDICAL AID? 6 -- 3. WHY DOES UMA MATTER? 7 -- 3.1. HUMAN RIGHTS 7 -- 3.2. PUBLIC HEALTH 7 -- 3.3. COSTS 7 -- 4. WHY THIS REPORT? 7 -- 5. HOW MANY INDIVIDUALS ARE CONCERNED? 9 -- 6. HOW DOES IT WORK? 11 -- 6.1. WHAT ARE THE CONDITIONS FOR UMA AGREEMENT? 11 -- 6.1.1. Territoriality 11 -- 6.1.2. Social enquiry to assess indigence 11 -- 6.1.3. Medical certificate for UMA? 13 -- 6.2. WHAT DECISION CAN THE CPAS – OCMW MAKE? 13 -- 6.2.1. UMA agreement or rejection 13 -- 6.2.2. Defining the extent and the duration of the coverage 14 -- 7. HOW MUCH DOES IT COST? 14 -- 7.1. WHO IS THE PAYER? 14 -- 7.2. WHAT IS THE ANNUAL BUDGET? 14 -- 7.3. IS THERE EVIDENCE FOR MEDICAL TOURISM? 15 -- 8. WHAT ARE THE STRENGTHS OF THE CURRENT ORGANISATION? 18 -- 8.1. HEALTH CARE 18 -- 8.2. ACTIVATION OF UMA 18 -- 8.3. CONNECTING ROLE OF CPAS – OCMW 18 -- 9. WHAT ARE THE DIFFICULTIES OF THE CURRENT ORGANIZATION? 19 -- 9.1. VARIATIONS IN SOCIAL ENQUIRY 19 -- 9.1.1. Variation in indigence assessment 19 -- 9.1.2. Great variation in rejection rate 19 -- 9.2. VARIATIONS IN ENTITLEMENT TO HEALTH CARE 19 -- 9.2.1. Global or restricted 19 -- 9.2.2. INAMI – RIZIV nomenclature or more 19 -- 9.2.3. Short or long 19 -- 9.3. ADMINISTRATIVE BURDEN 20 -- 9.3.1. Social enquiry 20 -- 9.3.2. Territoriality 20 -- 9.3.3. Health care delivered before an UMA agreement 20 -- 9.3.4. MediPrima 20 -- 9.3.5. Human resources 21 -- 9.4. THE NAME UMA GENERATES CONFUSION AND EXCLUSION 21 -- 9.5. FREE CHOICE OF MEDICAL DOCTOR 21 -- 9.6. DIFFICULT COMMUNICATION 21 -- 9.7. DIFFICULT MONITORING OF PRACTICES AND COSTS 21 -- 10. RECOMMENDATIONS FOR A REFORM OF UMA 22 -- RECOMMENDATIONS 27 -- BIBLIOGRAPHY 28
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".