Prevalence, Risk Factors, and Interventions of Postpartum Depression in Refugees and Asylum-Seeking Women: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Refugee women are at an increased risk of developing postpartum depression (PPD) due to a combination of various psychosocial stressors. This systematic review aimed to outline the prevalence of PPD among refugee women and explore related risk factors and interventions currently in practice. METHODS: A search was conducted using MEDLINE, Embase, PsycINFO, CINAHL, and Core Collection (Web of Science) for articles published until August 2022, yielding 1,678 records. RESULTS: The prevalence of refugee and asylum-seeking women was 22.5% (n = 657/2,922), while the prevalence of non-refugee/asylum-seeking women with PPD was 17.5% (n = 400/2,285). Refugee/asylum-seeking women face a unique set of issues such as domestic abuse, separation and lack of support, stress, pre-migrational experiences, prior history of mental illness, low income, and discrimination. Refugee/asylum-seeking women may benefit from support groups, individual support, self-coping mechanisms, and familial support. CONCLUSION: This review identifies that a higher prevalence of PPD in refugee and asylum-seeking women compared to other groups can potentially be attributed to the unique risk factors they face. This warrants the need for further research as studies on interventions for this condition are limited among this population.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".