The mental health of migrants living in limbo: A mixed-methods systematic review with meta-analysis
Bibliographic record
Abstract
The number of forcibly displaced people has more than doubled over the past decade. Many people fleeing are left in limbo without a secure pathway to citizenship or residency. This mixed-methods systematic review reports the prevalence of mental disorders in migrants living in limbo, the association between limbo and mental illness, and the experiences of these migrants in high income countries. We searched electronic databases for quantitative and qualitative studies published after January 1, 2010, on mental illness in precarious migrants living in HICs and performed a meta-analysis of prevalence rates. Fifty-eight articles met inclusion criteria. The meta-analysis yielded prevalence rates of 43.0 % for anxiety disorders (95 % CI 29.0-57.0), 49.5 % for depression (40.9-58.0) and 40.8 % for posttraumatic stress disorder (30.7-50.9). Having an insecure status was associated with higher rates of mental illness in most studies comparing migrants in limbo to those with secure status. Six themes emerged from the qualitative synthesis: the threat of deportation, uncertainty, social exclusion, stigmatization, social connection and religion. Clinicians should take an ecosocial approach to care that attends to stressors and symptoms. Furthermore, policymakers can mitigate the development of mental disorders among migrants by adopting policies that ensure rapid pathways to protected status.
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 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 | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".