Prevalence and risk of mental disorders in the perinatal period among migrant women: a systematic review and meta-analysis
Bibliographic record
Abstract
This study was conducted in order to evaluate the prevalence and risk of mental disorders in the perinatal period among migrant women. Six databases (including MEDLINE) were searched from inception to October 19th, 2015, in addition to citation tracking. Studies were eligible if mental disorders were assessed with validated tools during pregnancy and up to 1 year postpartum among women born outside of the study country. Of 3241 abstracts screened, 53 met the inclusion criteria for the review. Only three studies investigated a mental disorder other than depression. Unadjusted odds ratios were pooled using random effects meta-analysis for elevated depression symptoms during pregnancy (n = 12) and the postpartum (n = 24), stratified by study country due to heterogeneity. Studies from Canada found an increased risk for antenatal (OR = 1.86, 95% CIs 1.32-2.62) and postnatal elevated depression symptoms (OR = 1.98, 95% CIs 1.57-2.49) associated with migrant status. Studies from the USA found a decreased risk of antenatal elevated depression symptoms (OR = 0.71, 95% CIs 0.51-0.99), and studies from the USA and Australia found no association between migrant status and postnatal elevated depression symptoms. Low social support, minority ethnicity, low socioeconomic status, lack of proficiency in host country language and refugee or asylum-seeking status all put migrant populations at increased risk of perinatal mental disorders.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".