Concepts for migration-sensitive health monitoring
Bibliographic record
Abstract
According to microcensus data, nearly one quarter of the German population has a migration background. This means that either themselves or at least one parent was born without German citizenship. Based on the currently available data and due to the underrepresentation of specific population groups, representative findings on the health of the total population residing in Germany are only possible to a limited degree. Against this backdrop, the Robert Koch Institute initiated the Improving Health Monitoring in Migrant Populations (IMIRA) project. The project aims to establish a migration-sensitive health monitoring system and to better represent people with a migration background in health surveys conducted by the Robert Koch Institute. In this context it is crucial to review and further develop relevant migration-sensitive concepts and appropriate surveying instruments. To achieve this, the concepts of acculturation, discrimination, religion and subjective social status were selected. This article theoretically embeds these concepts. Furthermore, we describe their application in epidemiology as well as provide a proposal on how to measure and operationalise these concepts. Moreover, recommendations for action are provided regarding the potential application of these concepts in health monitoring at the Robert Koch Institute.
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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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".