Improving the information base regarding the health of people with a migration background. Project description and initial findings from IMIRA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 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 | 0.000 |
| 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 it