Integration of migrant and refugee data in health information systems in Europe: advancing evidence, policy and practice
Bibliographic record
Abstract
Coverage of migrant and refugee data is incomplete and of insufficient quality in European health information systems. This is not because we lack the knowledge or technology. Rather, it is due to various political factors at local, national and European levels, which hinder the implementation of existing knowledge and guidelines. This reflects the low political priority given to the topic, and also complex governance challenges associated with migration and displacement. We review recent evidence, guidelines, and policies to propose four approaches that will advance science, policy, and practice. First, we call for strategies that ensure that data is collected, analyzed and disseminated systematically. Second, we propose methods to safeguard privacy while combining data from multiple sources. Third, we set out how to enable survey methods that take account of the groups' diversity. Fourth, we emphasize the need to engage migrants and refugees in decisions about their own health data. Based on these approaches, we propose a change management approach that narrows the gap between knowledge and action to create healthcare policies and practices that are truly inclusive of migrants and refugees. We thereby offer an agenda that will better serve public health needs, including those of migrants and refugees and advance equity in European health systems. Funding: No specific funding received.
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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 | 0.052 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".