Prevalence of Depression among Migrants: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
As the number of migrants worldwide increases, it is worthwhile to examine the extent to which depression has affected this group of often vulnerable individuals. The purpose of this systematic review and meta-analysis is to examine the aggregate prevalence of depression among international migrants and to explore the variations in prevalence with demographic and educational factors. A search was conducted on the online databases PubMed and ScienceDirect whole using the terms "depression", "depressive disorder", "immigration", "immigrant", "migration", and "migrant". A total of 25 studies met our inclusion criteria. A random-effects model meta-analysis calculated an aggregate prevalence of 15.6% among migrants. Heterogeneity was identified by meta-regression and subgroup analyses, and the level of educational attainment, employment status, and length of residency spent in country of migration were found to be significant moderators contributing to depression prevalence. In conclusion, newly arrived migrants appear to be susceptible to developing depression and it is imperative that more in the form of preventive strategies and increased assistance be incorporated to ensure their psychological wellbeing and improve their mental health outcomes. Further research should be conducted to better understand the risk of psychiatric disorders among members of this subpopulation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".