International migration and adverse birth outcomes: role of ethnicity, region of origin and destination
Bibliographic record
Abstract
BACKGROUND: The literature on international migration and birth outcomes shows mixed results. This study examined whether low birth weight (LBW) and preterm birth differed between non-migrants and migrant subgroups, defined by race/ethnicity and world region of origin and destination. METHODS: A systematic review and meta-regression analyses were conducted using three-level logistic models to account for the heterogeneity between studies and between subgroups within studies. RESULTS: Twenty-four studies, involving more than 30 million singleton births, met the inclusion criteria. Compared with US-born black women, black migrant women were at lower odds of delivering LBW and preterm birth babies. Hispanic migrants also exhibited lower odds for these outcomes, but Asian and white migrants did not. Sub-Saharan African and Latin-American and Caribbean women were at higher odds of delivering LBW babies in Europe but not in the USA and south-central Asians were at higher odds in both continents, compared with the native-born populations. CONCLUSIONS: The association between migration and adverse birth outcomes varies by migrant subgroup and it is sensitive to the definition of the migrant and reference groups.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".