Refugees, Family Dynamics, and Resilience: Integrating Systemic Disruptions and Individual Coping Mechanisms
Bibliographic record
Abstract
Migration reshapes family dynamics, emotional well-being, and identity negotiation, disrupting caregiving structures, communication patterns, and intergenerational relationships. This study examines how refugees and their families navigate these systemic disruptions and employ coping mechanisms to foster resilience. Drawing on Family Systems Theory and Stress and Coping Theory, this research integrates macro- and micro-level perspectives to analyze migration as both a structural force affecting family units and an individual psychological challenge. Using a qualitative approach, the findings reveal how physical separation, caregiving reconfigurations, and intergenerational tensions redefine familial roles and emotional bonds. Refugees employ cultural frame switching, bilingual adaptation, and identity negotiation strategies to balance heritage preservation with host culture integration. Community support networks and emotional resilience emerge as critical factors in mitigating migration-induced stress. This study extends traditional models such as Berry’s acculturation framework and transnational family theories by emphasizing the interplay between systemic family disruptions and personal adaptation processes. The findings contribute to family research by bridging systemic and individual responses, offering policy implications for expediting family reunification, developing culturally responsive mental health services, and designing intergenerational integration programs. This study underscores the need for holistic, family-centered migration policies and support systems to enhance refugee family well-being and long-term resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".