German Health Update Fokus (GEDA Fokus) among Residents with Croatian, Italian, Polish, Syrian, or Turkish Citizenship in Germany: Protocol for a Multilingual Mixed-Mode Interview Survey
Bibliographic record
Abstract
BACKGROUND: Germany has a long history of migration. In 2020, more than 1 person in every 4 people had a statistically defined, so-called migration background in Germany, meaning that the person or at least one of their parents was born with a citizenship other than German citizenship. People with a history of migration are not represented proportionately to the population within public health monitoring at the Robert Koch Institute, thus impeding differentiated analyses of migration and health. To develop strategies for improving the inclusion of people with a history of migration in health surveys, we conducted a feasibility study in 2018. The lessons learned were implemented in the health interview survey German Health Update (Gesundheit in Deutschland aktuell [GEDA]) Fokus, which was conducted among people with selected citizenships representing the major migrant groups in Germany. OBJECTIVE: GEDA Fokus aimed to collect comprehensive data on the health status and social, migration-related, and structural factors among people with selected citizenships to enable differentiated explanations of the associations between migration-related aspects and their impact on migrant health. METHODS: GEDA Fokus is an interview survey among people with Croatian, Italian, Polish, Syrian, or Turkish citizenship living in Germany aged 18-79 years, with a targeted sample size of 1200 participants per group. The gross sample of 33,436 people was drawn from the residents' registration offices of 99 German municipalities based on citizenship. Sequentially, multiple modes of administration were offered. The questionnaire was available for self-administration (web-based and paper-based); in larger municipalities, personal or phone interviews were possible later on. Study documents and the questionnaire were bilingual-in German and the respective translation language depending on the citizenship. Data were collected from November 2021 to May 2022. RESULTS: Overall, 6038 respondents participated in the survey, of whom 2983 (49.4%) were female. The median age was 39 years; the median duration of residence in Germany was 10 years, with 19.69% (1189/6038) of the sample being born in Germany. The overall response rate was 18.4% (American Association for Public Opinion Research [AAPOR] response rate 1) and was 6.8% higher in the municipalities where personal interviews were offered (19.3% vs 12.5%). Overall, 78.12% (4717/6038) of the participants self-administered the questionnaire, whereas 21.88% (1321/6038) took part in personal interviews. In total, 41.85% (2527/6038) of the participants answered the questionnaire in the German language only, 16.69% (1008/6038) exclusively used the translation. CONCLUSIONS: Offering different modes of administration, as well as multiple study languages, enabled us to recruit a heterogeneous sample of people with a history of migration. The data collected will allow differentiated analyses of the role and interplay of migration-related and social determinants of health and their impact on the health status of people with selected citizenships. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43503.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".