Improving the information base regarding the health of people with a migration background. Project description and initial findings from IMIRA
Bibliographic record
Abstract
Germany is an immigration country and nearly a quarter of its population has a migration background. Thus, there is increasingly a need for reliable information on the health situation of people with a migration background. The Robert Koch Institute is in charge of expanding its health monitoring to improve the representation of people with a migration background in interview and examination surveys. Studies adequately need to reflect the health status of people with a migration background and currently the Robert Koch Institute's representative interview and examination surveys for adults do not fully achieve this. At the end of 2016, therefore, the Improving Health Monitoring in Migrant Populations (IMIRA) project was initiated aiming to expand the Robert Koch Institute's health monitoring to people with migration background and improve their involvement in health surveys in the long-term. This includes carrying out two feasibility studies to test strategies to reach and recruit people with migration background for interview surveys and develop measures to overcome language barriers in examination surveys. In order to expand health reporting on migration and health, a reporting concept and a core indicator set will be developed and the potential of (secondary) data sources will be tested. Furthermore, plans foresee the testing and further development of relevant specific migration sensitive survey instruments and indicators, as well as increasing networking with relevant stakeholders.
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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 | 0.059 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".