Building Responsive Health Systems to Help Communities Affected by Migration: An International Delphi Consensus
Bibliographic record
Abstract
Persons affected by migration require health systems that are responsive and adaptable to the needs of both disadvantaged migrants and non-migrant populations. The objective of this study is to support health systems for populations affected by migration. MATERIALS AND METHODS: An international Delphi consensus process was used to identify policy approaches to improve health systems for populations affected by migration. Participants were leading migrant health experts from Americas, Europe, Middle East, Asia, and Australasia. We calculated average ranking scores and qualitatively analyzed open-ended questions. RESULTS: Participants identified the following key areas as priorities for policy development: health inequities, system discrimination, migrant related health data, disadvantaged migrant sub-groups, and considerations for disadvantaged non-migrant populations. Highly ranked items to improve health systems were: Health Equity Impact Assessment, evidence based guidelines, and the International Organization for Migration annual reports. DISCUSSION: Policy makers need tools, data and resources to address health systems challenges. Policies need to avoid preventable deaths of migrants and barriers to basic health services.
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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 | 0.149 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 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".