Perceived loneliness: Why are Syrian refugees more lonely than other newly arrived migrants in Germany?
Bibliographic record
Abstract
Abstract Migration often impacts the mental and emotional health of those needing to move from their home countries. Studies have focused on migrants’ levels of distress or well-being, and recent research looks at older migrants’ experience with loneliness. What has yet to be researched is how different migrant groups experience loneliness, and how these feelings are affected by the contexts of leaving one country and reception in another. Drawing on the theoretical framework of integration, this article asks whether newly arrived refugees in Germany differ in their perception of loneliness from other newly arrived migrants. It examines these perceptions as related to social contacts and the context—and interplay—of exit and reception. Using OLS regressions with data from the Recent Immigration Processes and Early Integration Trajectories in Germany (ENTRA) project, we find that Syrian refugees have higher levels of loneliness than migrant groups from Poland, Italy, and Turkey. The difference is largely attributable to Syrians not having local German contacts, surviving traumatic experiences at home, and migrating specifically for physical safety. We also find that discrimination and not being in the labor force are determinants of loneliness across all four groups, and that even when considering migrant origins and other effects, having local social contacts lowers levels of loneliness. Our results point to migration policies, such as those related to family reunification and labor market access, for producing inequalities in loneliness between Syrian refugees and other migrants in Germany.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".