Netzwerkanalyse zur Integration von geflüchteten Schwangeren in das Gesundheitssystem in Köln
Bibliographic record
Abstract
Summary Network analysis concerning the integration of pregnant refugee women in the health care system of Cologne The city of Cologne registered more than 13,000 refugees in 2016, among them many women who were already pregnant at the time of their arrival or who got pregnant in Germany. This imposes continuous challenges on the German health care system. In a state analysis, the health care situation, means for integration and cooperation between actors within the health care and social welfare systems were explored. The results of guideline-based expert interviews with representatives, involved in the care of pregnant refugees, show a detailed picture of the situation, where the home manager of refugee accommodations takes a central position. Forming inter-professional and multi-professional teams in refugee accommodations is important to accompany and support the home managers in their duties. One obstacle restraining the integration of pregnant refuges is the lack of translation services and involvement in social structures. Communication problems between the different system levels prevent full inclusion and cooperation between all actors involved. Many concepts constructed are not based on the women’s needs and therefore cannot be sufficiently coordinated. The adaptation of these concepts to support the integration of female refugees needs to be optimized through the participation of all system levels, as well as the incorporation of already proven concepts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".